topic_modelling_LDA.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "topic_modelling_LDA.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMv/sCXfZtUFNBALMVjx68G",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aawanRahman/latent-dirichlet-allocation/blob/master/topic_modelling_LDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7xXNZTxvBu-D",
        "colab_type": "text"
      },
      "source": [
        "**The whole Process is done on google colab.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cR27qWjjI4Y4",
        "colab_type": "code",
        "outputId": "c4a4dfa6-a134-4e0b-8f70-fe18ac4888d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9O1SFKZ9QJ1P",
        "colab_type": "code",
        "outputId": "e0249ee3-c233-4354-c2e1-13db4775b25d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "#checking the device property...\n",
        "from tensorflow.python.client import device_lib\n",
        "print(device_lib.list_local_devices())"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[name: \"/device:CPU:0\"\n",
            "device_type: \"CPU\"\n",
            "memory_limit: 268435456\n",
            "locality {\n",
            "}\n",
            "incarnation: 7906012918408224510\n",
            ", name: \"/device:XLA_CPU:0\"\n",
            "device_type: \"XLA_CPU\"\n",
            "memory_limit: 17179869184\n",
            "locality {\n",
            "}\n",
            "incarnation: 10152054765999907514\n",
            "physical_device_desc: \"device: XLA_CPU device\"\n",
            ", name: \"/device:XLA_GPU:0\"\n",
            "device_type: \"XLA_GPU\"\n",
            "memory_limit: 17179869184\n",
            "locality {\n",
            "}\n",
            "incarnation: 2408798757239145726\n",
            "physical_device_desc: \"device: XLA_GPU device\"\n",
            ", name: \"/device:GPU:0\"\n",
            "device_type: \"GPU\"\n",
            "memory_limit: 7304675328\n",
            "locality {\n",
            "  bus_id: 1\n",
            "  links {\n",
            "  }\n",
            "}\n",
            "incarnation: 5544276523308925350\n",
            "physical_device_desc: \"device: 0, name: Tesla P4, pci bus id: 0000:00:04.0, compute capability: 6.1\"\n",
            "]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tgU2qE9LB5l8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Library load"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w-weDxW2B8eU",
        "colab_type": "text"
      },
      "source": [
        "## **Library load**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2whZFZPKQnLS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from nltk.tokenize import RegexpTokenizer\n",
        "\n",
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "from gensim import corpora, models\n",
        "\n",
        "\n",
        "import gensim\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "53IrIc3nfTfo",
        "colab_type": "text"
      },
      "source": [
        "## **Dataset Load**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNr41vaHTQlB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pd.read_csv('/content/drive/My Drive/topic modelling/voted-kaggle-dataset.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jPkCztyKT4FP",
        "colab_type": "code",
        "outputId": "302e36cf-dd91-4912-929b-4c5c80fcedae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "print(dataset.head())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                         Title  ...                                        Description\n",
            "0  Credit Card Fraud Detection  ...  The datasets contains transactions made by cre...\n",
            "1     European Soccer Database  ...  The ultimate Soccer database for data analysis...\n",
            "2      TMDB 5000 Movie Dataset  ...  Background\\nWhat can we say about the success ...\n",
            "3    Human Resources Analytics  ...  This dataset is simulated\\nWhy are our best an...\n",
            "4    Global Terrorism Database  ...  Context\\nInformation on more than 170,000 Terr...\n",
            "\n",
            "[5 rows x 15 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ohhhthyoUj-v",
        "colab_type": "code",
        "outputId": "892f7e1f-9b3a-47a5-c374-bac432da4725",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        }
      },
      "source": [
        "dataset.iloc[0:3,:]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Title</th>\n",
              "      <th>Subtitle</th>\n",
              "      <th>Owner</th>\n",
              "      <th>Votes</th>\n",
              "      <th>Last Update</th>\n",
              "      <th>Tags</th>\n",
              "      <th>Data Type</th>\n",
              "      <th>Size</th>\n",
              "      <th>License</th>\n",
              "      <th>Views</th>\n",
              "      <th>Download</th>\n",
              "      <th>Kernels</th>\n",
              "      <th>Topics</th>\n",
              "      <th>URL</th>\n",
              "      <th>Description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Credit Card Fraud Detection</td>\n",
              "      <td>Anonymized credit card transactions labeled as...</td>\n",
              "      <td>Machine Learning Group - ULB</td>\n",
              "      <td>1233</td>\n",
              "      <td>2016-11-05</td>\n",
              "      <td>crime\\nfinance</td>\n",
              "      <td>CSV</td>\n",
              "      <td>144 MB</td>\n",
              "      <td>ODbL</td>\n",
              "      <td>440,221 views</td>\n",
              "      <td>52,793 downloads</td>\n",
              "      <td>1,778 kernels</td>\n",
              "      <td>26 topics</td>\n",
              "      <td>https://www.kaggle.com/mlg-ulb/creditcardfraud</td>\n",
              "      <td>The datasets contains transactions made by cre...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>European Soccer Database</td>\n",
              "      <td>25k+ matches, players &amp; teams attributes for E...</td>\n",
              "      <td>Hugo Mathien</td>\n",
              "      <td>1035</td>\n",
              "      <td>2016-10-24</td>\n",
              "      <td>association football\\neurope</td>\n",
              "      <td>SQLite</td>\n",
              "      <td>299 MB</td>\n",
              "      <td>ODbL</td>\n",
              "      <td>393,924 views</td>\n",
              "      <td>46,025 downloads</td>\n",
              "      <td>1,459 kernels</td>\n",
              "      <td>75 topics</td>\n",
              "      <td>https://www.kaggle.com/hugomathien/soccer</td>\n",
              "      <td>The ultimate Soccer database for data analysis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TMDB 5000 Movie Dataset</td>\n",
              "      <td>Metadata on ~5,000 movies from TMDb</td>\n",
              "      <td>The Movie Database (TMDb)</td>\n",
              "      <td>1018</td>\n",
              "      <td>2017-09-28</td>\n",
              "      <td>film</td>\n",
              "      <td>CSV</td>\n",
              "      <td>44 MB</td>\n",
              "      <td>Other</td>\n",
              "      <td>444,535 views</td>\n",
              "      <td>61,705 downloads</td>\n",
              "      <td>1,394 kernels</td>\n",
              "      <td>46 topics</td>\n",
              "      <td>https://www.kaggle.com/tmdb/tmdb-movie-metadata</td>\n",
              "      <td>Background\\nWhat can we say about the success ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Title  ...                                        Description\n",
              "0  Credit Card Fraud Detection  ...  The datasets contains transactions made by cre...\n",
              "1     European Soccer Database  ...  The ultimate Soccer database for data analysis...\n",
              "2      TMDB 5000 Movie Dataset  ...  Background\\nWhat can we say about the success ...\n",
              "\n",
              "[3 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PKf2ZH2nT-yc",
        "colab_type": "code",
        "outputId": "9b8ac6b5-ab21-4045-b922-5aeb33ccf230",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "dataset.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2885 entries, 0 to 2884\n",
            "Data columns (total 15 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   Title        2885 non-null   object\n",
            " 1   Subtitle     2413 non-null   object\n",
            " 2   Owner        2885 non-null   object\n",
            " 3   Votes        2885 non-null   int64 \n",
            " 4   Last Update  2885 non-null   object\n",
            " 5   Tags         1700 non-null   object\n",
            " 6   Data Type    2885 non-null   object\n",
            " 7   Size         2885 non-null   object\n",
            " 8   License      2885 non-null   object\n",
            " 9   Views        2874 non-null   object\n",
            " 10  Download     2803 non-null   object\n",
            " 11  Kernels      1276 non-null   object\n",
            " 12  Topics       2243 non-null   object\n",
            " 13  URL          2885 non-null   object\n",
            " 14  Description  2874 non-null   object\n",
            "dtypes: int64(1), object(14)\n",
            "memory usage: 338.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t5zTZXE8VKl8",
        "colab_type": "text"
      },
      "source": [
        "# **Data Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gtjOHpZTKwAL",
        "colab_type": "code",
        "outputId": "9aa91ffe-9000-4e2c-f4b9-2ad863008c66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "dataset.columns.values"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Title', 'Subtitle', 'Owner', 'Votes', 'Last Update', 'Tags',\n",
              "       'Data Type', 'Size', 'License', 'Views', 'Download', 'Kernels',\n",
              "       'Topics', 'URL', 'Description'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7DhB_P5FKzXO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "modified_dataset = dataset.loc[ : , ['Title','Subtitle','Description']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pqCML9XpK4UQ",
        "colab_type": "code",
        "outputId": "cf2c0f80-4edb-4875-c94c-74085acfe348",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "modified_dataset.iloc[0:5,:]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Title</th>\n",
              "      <th>Subtitle</th>\n",
              "      <th>Description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Credit Card Fraud Detection</td>\n",
              "      <td>Anonymized credit card transactions labeled as...</td>\n",
              "      <td>The datasets contains transactions made by cre...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>European Soccer Database</td>\n",
              "      <td>25k+ matches, players &amp; teams attributes for E...</td>\n",
              "      <td>The ultimate Soccer database for data analysis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TMDB 5000 Movie Dataset</td>\n",
              "      <td>Metadata on ~5,000 movies from TMDb</td>\n",
              "      <td>Background\\nWhat can we say about the success ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Human Resources Analytics</td>\n",
              "      <td>Why are our best and most experienced employee...</td>\n",
              "      <td>This dataset is simulated\\nWhy are our best an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Global Terrorism Database</td>\n",
              "      <td>More than 170,000 terrorist attacks worldwide,...</td>\n",
              "      <td>Context\\nInformation on more than 170,000 Terr...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Title  ...                                        Description\n",
              "0  Credit Card Fraud Detection  ...  The datasets contains transactions made by cre...\n",
              "1     European Soccer Database  ...  The ultimate Soccer database for data analysis...\n",
              "2      TMDB 5000 Movie Dataset  ...  Background\\nWhat can we say about the success ...\n",
              "3    Human Resources Analytics  ...  This dataset is simulated\\nWhy are our best an...\n",
              "4    Global Terrorism Database  ...  Context\\nInformation on more than 170,000 Terr...\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sQ0zPtu2K_JT",
        "colab_type": "code",
        "outputId": "5eb81671-2ec0-4c7d-d1ee-a47abaf2640c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "print(modified_dataset.info())"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2885 entries, 0 to 2884\n",
            "Data columns (total 3 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   Title        2885 non-null   object\n",
            " 1   Subtitle     2413 non-null   object\n",
            " 2   Description  2874 non-null   object\n",
            "dtypes: object(3)\n",
            "memory usage: 67.7+ KB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ofi--9zUVIRp",
        "colab_type": "code",
        "outputId": "d8a8baf1-81ac-46b3-cac1-fbef61455f80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "modified_dataset.isnull()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Title</th>\n",
              "      <th>Subtitle</th>\n",
              "      <th>Description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2880</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2881</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2882</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2883</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2884</th>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2885 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Title  Subtitle  Description\n",
              "0     False     False        False\n",
              "1     False     False        False\n",
              "2     False     False        False\n",
              "3     False     False        False\n",
              "4     False     False        False\n",
              "...     ...       ...          ...\n",
              "2880  False      True        False\n",
              "2881  False      True        False\n",
              "2882  False      True        False\n",
              "2883  False      True        False\n",
              "2884  False      True        False\n",
              "\n",
              "[2885 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hy5Cm7mKWcew",
        "colab_type": "code",
        "outputId": "0d50a0a0-79e4-4c9c-8adb-b9be8e1cc636",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "print(modified_dataset.isnull().sum())\n",
        "print(\"total null values : \",sum(modified_dataset.isnull().values.ravel()))\n",
        "print(\"total number of rows containing null values : \", sum([True for idx,row in modified_dataset.iterrows() if any(row.isnull())]))\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Title            0\n",
            "Subtitle       472\n",
            "Description     11\n",
            "dtype: int64\n",
            "total null values :  483\n",
            "total number of rows containing null values :  482\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GM8ohUeWXL7c",
        "colab_type": "text"
      },
      "source": [
        "now we will remove the rows containing any missing values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GfYXZ6YZW_Iz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "modified_dataset = modified_dataset.dropna()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QeOs41fDXCcw",
        "colab_type": "code",
        "outputId": "e0aeed38-34ee-409b-b777-1ad8b8976250",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(\"total null values : \",sum(modified_dataset.isnull().values.ravel()))\n",
        "print(\"total number of rows containing null values : \", sum([True for idx,row in modified_dataset.iterrows() if any(row.isnull())]))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total null values :  0\n",
            "total number of rows containing null values :  0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UQxoojIjZCWm",
        "colab_type": "text"
      },
      "source": [
        "selecting  the columns that are  used in training phase."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plv25XOjcGLt",
        "colab_type": "code",
        "outputId": "a9e19780-f773-40ee-a968-395754ca6015",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "print(modified_dataset.info())"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2403 entries, 0 to 2879\n",
            "Data columns (total 3 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   Title        2403 non-null   object\n",
            " 1   Subtitle     2403 non-null   object\n",
            " 2   Description  2403 non-null   object\n",
            "dtypes: object(3)\n",
            "memory usage: 75.1+ KB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-tY4bKN3cWd9",
        "colab_type": "text"
      },
      "source": [
        "removing punctuation and stopwords and lower casing "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "17k17JgXcWDV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import string\n",
        "import re"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5F5eY9cxc4U4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "remove_digits = str.maketrans('', '', string.digits) # Set of all digits"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0DlO90HPc_Rl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for column in ['Title','Subtitle','Description']:\n",
        "  modified_dataset[column] = modified_dataset[column].map(lambda x : x.lower())\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FNIFMOqBfXQm",
        "colab_type": "code",
        "outputId": "e6f19f3a-ad30-4ab3-dcd4-173f1cf4e15f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "modified_dataset.iloc[0:3,:]"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Title</th>\n",
              "      <th>Subtitle</th>\n",
              "      <th>Description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>credit card fraud detection</td>\n",
              "      <td>anonymized credit card transactions labeled as...</td>\n",
              "      <td>the datasets contains transactions made by cre...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>european soccer database</td>\n",
              "      <td>25k+ matches, players &amp; teams attributes for e...</td>\n",
              "      <td>the ultimate soccer database for data analysis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tmdb 5000 movie dataset</td>\n",
              "      <td>metadata on ~5,000 movies from tmdb</td>\n",
              "      <td>background\\nwhat can we say about the success ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Title  ...                                        Description\n",
              "0  credit card fraud detection  ...  the datasets contains transactions made by cre...\n",
              "1     european soccer database  ...  the ultimate soccer database for data analysis...\n",
              "2      tmdb 5000 movie dataset  ...  background\\nwhat can we say about the success ...\n",
              "\n",
              "[3 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ohe45suj7EG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "exclude = '[!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~]'\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bwu1GOM2fsWd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for column in ['Title','Subtitle','Description']:\n",
        "  modified_dataset[column] = modified_dataset[column].map(lambda x : x.translate(remove_digits))\n",
        "  modified_dataset[column] = modified_dataset[column].map(lambda x : re.sub(str(exclude), '', x))\n",
        "\n",
        "\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZRHQkJ-XisOv",
        "colab_type": "code",
        "outputId": "96b0bd3b-0520-489a-cd6d-9d739a53ee46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "modified_dataset.iloc[0:3,:]"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Title</th>\n",
              "      <th>Subtitle</th>\n",
              "      <th>Description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>credit card fraud detection</td>\n",
              "      <td>anonymized credit card transactions labeled as...</td>\n",
              "      <td>the datasets contains transactions made by cre...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>european soccer database</td>\n",
              "      <td>k matches players  teams attributes for europe...</td>\n",
              "      <td>the ultimate soccer database for data analysis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tmdb  movie dataset</td>\n",
              "      <td>metadata on  movies from tmdb</td>\n",
              "      <td>background\\nwhat can we say about the success ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Title  ...                                        Description\n",
              "0  credit card fraud detection  ...  the datasets contains transactions made by cre...\n",
              "1     european soccer database  ...  the ultimate soccer database for data analysis...\n",
              "2          tmdb  movie dataset  ...  background\\nwhat can we say about the success ...\n",
              "\n",
              "[3 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r9Uph2k8krcI",
        "colab_type": "text"
      },
      "source": [
        "now we use stemming and lemmalization for preprocessing the data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dqJ9EWOluiD3",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "counting the number of unique tags"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LqQ50P4Jum6d",
        "colab_type": "code",
        "outputId": "c46046bc-444a-4d18-e55a-a0a0d931fa80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tag_dataset = dataset['Tags']\n",
        "tag_dataset.isnull().sum()\n",
        "#tag_dataset=tag_dataset.dropna()\n",
        "tag_dataset.isnull().sum()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1185"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-nPNos4us_d",
        "colab_type": "code",
        "outputId": "ec6b7c87-ffb2-48f0-ada0-c3c094a96d68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(len(tag_dataset))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2885\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jnIOV15QutN2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def convert(lst): \n",
        "    return (lst.split(\"\\n\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTrb43rRutUb",
        "colab_type": "code",
        "outputId": "f409ca8d-2972-46ed-a8da-b8ee13ee0e16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "unique_tag = []\n",
        "for i in range(len(tag_dataset)):\n",
        "  tag_string = str(tag_dataset[i])\n",
        "  if tag_string != \"nan\" :\n",
        "    tag_word=convert(tag_string)\n",
        "    for j in range(len(tag_word)):\n",
        "      if tag_word[j] not in unique_tag:\n",
        "        unique_tag.append(tag_word[j])\n",
        "print(len(unique_tag))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "329\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTCNEo3uutSQ",
        "colab_type": "code",
        "outputId": "1420fc23-c62a-4695-8065-07633360c948",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for i in range(len(unique_tag)):\n",
        "  print(unique_tag[i])"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "crime\n",
            "finance\n",
            "association football\n",
            "europe\n",
            "film\n",
            "employment\n",
            "terrorism\n",
            "international relations\n",
            "history\n",
            "sociology\n",
            "artificial intelligence\n",
            "botany\n",
            "economics\n",
            "news agencies\n",
            "popular culture\n",
            "games and toys\n",
            "video games\n",
            "critical theory\n",
            "food and drink\n",
            "environment\n",
            "climate\n",
            "nutrition\n",
            "universities and colleges\n",
            "education\n",
            "healthcare\n",
            "linguistics\n",
            "internet\n",
            "emotion\n",
            "social sciences\n",
            "clothing\n",
            "image data\n",
            "multiclass classification\n",
            "object identification\n",
            "politics\n",
            "literature\n",
            "social groups\n",
            "war\n",
            "love\n",
            "home\n",
            "housing\n",
            "business\n",
            "demographics\n",
            "society\n",
            "mental health\n",
            "human genetics\n",
            "+ 2 more...\n",
            "personality\n",
            "brazil\n",
            "public health\n",
            "twitter\n",
            "aviation\n",
            "gender\n",
            "law\n",
            "psychometrics\n",
            "telecommunications\n",
            "languages\n",
            "road transport\n",
            "income\n",
            "space\n",
            "death\n",
            "cricket\n",
            "india\n",
            "humor\n",
            "mass media\n",
            "world\n",
            "sports\n",
            "sound technology\n",
            "statistics\n",
            "automobiles\n",
            "children\n",
            "pollution\n",
            "management\n",
            "united states\n",
            "violence\n",
            "basketball\n",
            "human-computer interaction\n",
            "celebrity\n",
            "humans\n",
            "astronomy\n",
            "reddit\n",
            "information technology\n",
            "baseball\n",
            "neuroscience\n",
            "parties\n",
            "cities\n",
            "board games\n",
            "music\n",
            "crowdfunding\n",
            "health\n",
            "money\n",
            "bigquery\n",
            "political science\n",
            "faith and traditions\n",
            "islam\n",
            "programming languages\n",
            "programming\n",
            "software engineering\n",
            "shipping\n",
            "epidemiology\n",
            "biography\n",
            "journalism\n",
            "firefighting\n",
            "historiography\n",
            "pre-trained model\n",
            "earth sciences\n",
            "computing and society\n",
            "horse racing\n",
            "tutorial\n",
            "weather\n",
            "energy\n",
            "writing\n",
            "american football\n",
            "countries\n",
            "time series\n",
            "geography\n",
            "taxi services\n",
            "beginner\n",
            "regression analysis\n",
            "books\n",
            "chemistry\n",
            "ecology\n",
            "animals\n",
            "plants\n",
            "object detection\n",
            "medicine\n",
            "machine learning\n",
            "oceans\n",
            "cycling\n",
            "government agencies\n",
            "immigration\n",
            "auto racing\n",
            "lending\n",
            "communication\n",
            "physics\n",
            "biology\n",
            "military\n",
            "life\n",
            "leisure\n",
            "hotels\n",
            "banking\n",
            "computers\n",
            "computer architecture\n",
            "real estate\n",
            "card games\n",
            "tennis\n",
            "biotechnology\n",
            "north america\n",
            "agriculture\n",
            "hobbies\n",
            "olympic games\n",
            "forestry\n",
            "acoustics\n",
            "consumer electronics\n",
            "supply chain\n",
            "arts and entertainment\n",
            "actors\n",
            "libraries\n",
            "orthopedic surgery\n",
            "pediatrics\n",
            "gerontology\n",
            "hospitals\n",
            "rail transport\n",
            "mathematics\n",
            "health sciences\n",
            "learning\n",
            "mobile web\n",
            "astronauts\n",
            "product\n",
            "dentistry\n",
            "russia\n",
            "government\n",
            "running\n",
            "walking\n",
            "electronics\n",
            "research\n",
            "databases\n",
            "illegal drugs\n",
            "globalization\n",
            "binary classification\n",
            "computer science\n",
            "electrical components\n",
            "industry\n",
            "comics\n",
            "water technology\n",
            "neurology\n",
            "pharmaceutical industry\n",
            "pharmaceuticals policy\n",
            "museums\n",
            "visual arts\n",
            "photography\n",
            "cognitive biases\n",
            "biases\n",
            "networks\n",
            "diseases\n",
            "web sites\n",
            "timelines\n",
            "marketing\n",
            "south america\n",
            "stochastic processes\n",
            "communities\n",
            "mining\n",
            "personal life\n",
            "computing\n",
            "alcohol\n",
            "transport\n",
            "geology\n",
            "africa\n",
            "nature\n",
            "veterinary medicine\n",
            "fishing\n",
            "environmental engineering\n",
            "public transport\n",
            "geometry\n",
            "electrical engineering\n",
            "automation\n",
            "ethics\n",
            "pharmacy\n",
            "ice hockey\n",
            "atmospheric sciences\n",
            "strategy\n",
            "companies\n",
            "weight training\n",
            "mountains\n",
            "encyclopedias\n",
            "knowledge\n",
            "covariance and correlation\n",
            "politicians\n",
            "deep learning\n",
            "entertainment\n",
            "vehicles\n",
            "people\n",
            "decision theory\n",
            "agronomy\n",
            "surveillance\n",
            "computer security\n",
            "civil engineering\n",
            "physical sciences\n",
            "presidents\n",
            "science and culture\n",
            "poetry\n",
            "oceanography\n",
            "product management\n",
            "military science\n",
            "technology forecasting\n",
            "categorical data\n",
            "scientific method\n",
            "cultural mythology\n",
            "lgbt\n",
            "robotics\n",
            "optics\n",
            "nlp\n",
            "text mining\n",
            "information\n",
            "spaceflight\n",
            "china\n",
            "composers\n",
            "musicians\n",
            "classification\n",
            "navigation\n",
            "manufacturing\n",
            "psychology\n",
            "asia\n",
            "microtechnology\n",
            "rugby league\n",
            "numbers\n",
            "dictionaries\n",
            "grammar\n",
            "renewable energy\n",
            "slaves\n",
            "schools and traditions\n",
            "australia\n",
            "traditions\n",
            "anthropology\n",
            "cultural studies\n",
            "exercise\n",
            "culture and humanities\n",
            "animation\n",
            "scientists\n",
            "classics\n",
            "research tools and topics\n",
            "engineering\n",
            "organizations\n",
            "aerospace engineering\n",
            "network analysis\n",
            "image processing\n",
            "object labeling\n",
            "survey analysis\n",
            "natural resources\n",
            "populated places\n",
            "relationships\n",
            "health law\n",
            "pets\n",
            "mechanical engineering\n",
            "gymnastics\n",
            "architecture\n",
            "painting\n",
            "sailing\n",
            "bodies of water\n",
            "safety\n",
            "reading\n",
            "counting\n",
            "golf\n",
            "text data\n",
            "data cleaning\n",
            "media studies\n",
            "digital media\n",
            "drawing\n",
            "lists of people\n",
            "puzzles\n",
            "christianity\n",
            "antarctica\n",
            "survival analysis\n",
            "ethnic groups\n",
            "chemical engineering\n",
            "data visualization\n",
            "storytelling\n",
            "lakes\n",
            "mathematics education\n",
            "sleep\n",
            "multiple regression\n",
            "decision tree\n",
            "computer engineering\n",
            "churn analysis\n",
            "logistic regression\n",
            "universe\n",
            "cartography\n",
            "construction\n",
            "mortality\n",
            "archives\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bjcnC6zfpF48",
        "colab_type": "code",
        "outputId": "3a2aeebd-6071-4a62-80a8-22784e99c6a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "tokenized_dataframe =  modified_dataset.apply(lambda row: nltk.word_tokenize(row['Description']), axis=1)\n",
        "print(type(tokenized_dataframe))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "<class 'pandas.core.series.Series'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkCSoDLypa8q",
        "colab_type": "code",
        "outputId": "9594e266-881b-4015-f36f-1225e4e73a2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print(tokenized_dataframe[0:3])\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    [the, datasets, contains, transactions, made, ...\n",
            "1    [the, ultimate, soccer, database, for, data, a...\n",
            "2    [background, what, can, we, say, about, the, s...\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nh_ybcw6x7hK",
        "colab_type": "code",
        "outputId": "4b085262-dd5e-44e1-f71d-e05e21c2b568",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#single word check......\n",
        "from nltk.stem import PorterStemmer \n",
        "ps = PorterStemmer() \n",
        "print(ps.stem('contains'))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "contain\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5wH2ziG2tn4V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def lemmatize_text(text):\n",
        "    return [ps.stem(w)  for w in text if len(w)>5]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uK4IXqZ4kqb_",
        "colab_type": "code",
        "outputId": "a423d41a-4a33-4b28-abd1-277c6d2e6833",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "nltk.download('wordnet')\n",
        "from nltk.stem import PorterStemmer \n",
        "ps = PorterStemmer() \n",
        "stemmed_dataset = tokenized_dataframe.apply(lemmatize_text)\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPsgY6LhuA4P",
        "colab_type": "code",
        "outputId": "804ff201-a91b-4bec-8b60-a82e5e3cda46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(type(stemmed_dataset))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.series.Series'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGvDcQr8uFH5",
        "colab_type": "code",
        "outputId": "568d8b33-576a-4779-fe45-99b8147a2331",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "stemmed_dataset[0:3]"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    [dataset, contain, transact, credit, septemb, ...\n",
              "1    [ultim, soccer, databas, analysi, machin, lear...\n",
              "2    [background, success, befor, releas, certain, ...\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-D-L2-HmozJ",
        "colab_type": "text"
      },
      "source": [
        "## **Exploratory Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LHzwXFTimdG-",
        "colab_type": "code",
        "outputId": "d31c3f4d-b9ca-4d8b-b751-484a92865b4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        }
      },
      "source": [
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "#dataset_words=''\n",
        "#for column in ['Title','Subtitle','Description']:\n",
        "dataset_words=''.join(list(str(stemmed_dataset.values)))\n",
        "print(type(dataset_words))\n",
        "wordcloud = WordCloud(width = 800, height = 500, \n",
        "                background_color ='white',  \n",
        "                min_font_size = 10).generate(dataset_words) \n",
        "\n",
        "plt.figure(figsize = (5, 5), facecolor = None) \n",
        "plt.imshow(wordcloud) \n",
        "plt.axis(\"off\") \n",
        "plt.tight_layout(pad = 0) \n",
        "  \n",
        "plt.show() "
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'str'>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAADvCAYAAADmQP9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEm8z/g+fFbC48HJKdgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Vd8vn1G2bBy",
        "colab_type": "text"
      },
      "source": [
        "## **Creating Dictionary and corpus for LDA model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WuBlxHPFiUgZ",
        "colab_type": "code",
        "outputId": "582a9e61-f925-48c3-dec6-9ccaa22744c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dictionary_of_words = gensim.corpora.Dictionary(stemmed_dataset)\n",
        "print(type(dictionary_of_words))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'gensim.corpora.dictionary.Dictionary'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-0YS0Ns61_e",
        "colab_type": "code",
        "outputId": "a266a327-b30a-4bee-895c-ce167c01ee19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(len(dictionary_of_words))\n"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "24620\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTVORc7OkvHk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Creatig coupus which contains  of word id mapping with word_frequency--->(word_id, word_frequency) \n",
        "word_corpus = [dictionary_of_words.doc2bow(word) for word in stemmed_dataset]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p7J6Jz4wlRnD",
        "colab_type": "code",
        "outputId": "a4cda1ca-3e34-4d20-c1d7-eedfe4c0c3bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for corp in word_corpus[:1]:\n",
        "  for id, freq in corp:\n",
        "    print(dictionary_of_words[id],freq)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "account 1\n",
            "accuraci 2\n",
            "amount 3\n",
            "analys 1\n",
            "andrea 1\n",
            "avail 1\n",
            "background 1\n",
            "between 1\n",
            "bontempi 1\n",
            "bruxel 1\n",
            "caelen 1\n",
            "calibr 1\n",
            "cardhold 1\n",
            "classif 2\n",
            "collabor 1\n",
            "collect 1\n",
            "compon 1\n",
            "comput 1\n",
            "confidenti 1\n",
            "confus 1\n",
            "contain 3\n",
            "costsenst 1\n",
            "credit 1\n",
            "current 1\n",
            "dataset 5\n",
            "detail 1\n",
            "detect 1\n",
            "dure 1\n",
            "elaps 1\n",
            "european 1\n",
            "exampledepend 1\n",
            "featur 7\n",
            "fraud 2\n",
            "gianluca 1\n",
            "highli 1\n",
            "httpmlgulbacb 1\n",
            "httpmlgulbacbeartml 1\n",
            "httpmlgulbacbebruf 1\n",
            "imbal 1\n",
            "inform 1\n",
            "intellig 1\n",
            "issu 1\n",
            "johnson 1\n",
            "learn 2\n",
            "machin 1\n",
            "matrix 1\n",
            "meaning 1\n",
            "measur 1\n",
            "mine 2\n",
            "numer 1\n",
            "obtain 1\n",
            "occur 1\n",
            "olivi 1\n",
            "origin 1\n",
            "otherwis 1\n",
            "pleas 1\n",
            "posit 1\n",
            "pozzolo 1\n",
            "precisionrecal 1\n",
            "present 1\n",
            "princip 1\n",
            "probabl 1\n",
            "project 1\n",
            "provid 1\n",
            "recommend 1\n",
            "relat 1\n",
            "research 1\n",
            "respons 1\n",
            "result 1\n",
            "second 1\n",
            "septemb 1\n",
            "symposium 1\n",
            "topic 1\n",
            "transact 7\n",
            "transform 2\n",
            "unbalanc 3\n",
            "undersampl 1\n",
            "unfortun 1\n",
            "université 1\n",
            "variabl 2\n",
            "worldlin 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4vpGa6cnE06",
        "colab_type": "text"
      },
      "source": [
        "## **Building LDA model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1MzCy-rOCUO0",
        "colab_type": "text"
      },
      "source": [
        "Total unique tag in the dataset is 329. so we first try with 329 number of topics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZsvs4eTmUbG",
        "colab_type": "code",
        "outputId": "280465c2-091a-4029-df27-e6e55310b5da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "lda_model = gensim.models.ldamodel.LdaModel(corpus=word_corpus,\n",
        "                                           id2word=dictionary_of_words,\n",
        "                                           num_topics=329, \n",
        "                                           random_state=101,\n",
        "                                           update_every=1,\n",
        "                                           chunksize=300,\n",
        "                                           passes=50,\n",
        "                                           alpha='auto',\n",
        "                                           per_word_topics=True)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/models/ldamodel.py:1023: RuntimeWarning: divide by zero encountered in log\n",
            "  diff = np.log(self.expElogbeta)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tyxx6vg9p8hJ",
        "colab_type": "code",
        "outputId": "d5a74e00-f054-4fc3-871b-d3845a3663d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(lda_model.print_topics())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[(139, '0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"'), (10, '0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"'), (92, '0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"'), (6, '0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"'), (154, '0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"'), (194, '0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"'), (318, '0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"'), (262, '0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"'), (14, '0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"'), (258, '0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"'), (165, '0.472*\"between\" + 0.111*\"across\" + 0.064*\"group\" + 0.037*\"track\" + 0.037*\"benefit\" + 0.031*\"washington\" + 0.029*\"francisco\" + 0.026*\"budget\" + 0.022*\"encourag\" + 0.017*\"constitut\"'), (312, '0.427*\"descript\" + 0.198*\"describ\" + 0.145*\"insid\" + 0.080*\"address\" + 0.028*\"specifi\" + 0.025*\"sometim\" + 0.018*\"provinc\" + 0.011*\"grow\" + 0.005*\"bottl\" + 0.003*\"accomplish\"'), (150, '0.349*\"research\" + 0.185*\"start\" + 0.175*\"without\" + 0.139*\"world\" + 0.050*\"match\" + 0.042*\"place\" + 0.010*\"everyth\" + 0.000*\"antun\" + 0.000*\"agradeço\" + 0.000*\"candidato\"'), (33, '0.306*\"gener\" + 0.278*\"metadata\" + 0.052*\"subset\" + 0.051*\"energi\" + 0.051*\"investig\" + 0.048*\"help\" + 0.035*\"commiss\" + 0.019*\"corpor\" + 0.016*\"notebook\" + 0.013*\"launch\"'), (302, '0.151*\"process\" + 0.116*\"within\" + 0.089*\"better\" + 0.086*\"singl\" + 0.081*\"understand\" + 0.077*\"pattern\" + 0.070*\"connect\" + 0.045*\"wonder\" + 0.029*\"complex\" + 0.027*\"movement\"'), (201, '0.260*\"origin\" + 0.180*\"extract\" + 0.169*\"version\" + 0.133*\"perform\" + 0.072*\"credit\" + 0.039*\"analys\" + 0.026*\"european\" + 0.018*\"skill\" + 0.015*\"entri\" + 0.012*\"centuri\"'), (227, '0.416*\"contain\" + 0.166*\"follow\" + 0.109*\"format\" + 0.092*\"exampl\" + 0.047*\"combin\" + 0.045*\"correspond\" + 0.034*\"actual\" + 0.031*\"howev\" + 0.014*\"titl\" + 0.007*\"readm\"'), (210, '0.195*\"includ\" + 0.085*\"provid\" + 0.082*\"databas\" + 0.079*\"public\" + 0.056*\"result\" + 0.055*\"chang\" + 0.043*\"individu\" + 0.040*\"document\" + 0.037*\"through\" + 0.037*\"import\"'), (183, '0.169*\"inform\" + 0.100*\"avail\" + 0.100*\"sourc\" + 0.069*\"creat\" + 0.062*\"analysi\" + 0.052*\"detail\" + 0.047*\"pleas\" + 0.043*\"interest\" + 0.036*\"relat\" + 0.031*\"current\"'), (327, '0.304*\"dataset\" + 0.134*\"context\" + 0.133*\"content\" + 0.125*\"acknowledg\" + 0.111*\"inspir\" + 0.051*\"differ\" + 0.028*\"thank\" + 0.011*\"visual\" + 0.011*\"includ\" + 0.010*\"compar\"')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hM3pa2bL-aTQ",
        "colab_type": "code",
        "outputId": "0f415100-b41a-4b11-b649-0fc0bb044e08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for idx, topic in lda_model.print_topics(-1):\n",
        "    print('Topic: {} \\nWords: {}'.format(idx, topic))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Topic: 0 \n",
            "Words: 0.754*\"univers\" + 0.117*\"institut\" + 0.037*\"regard\" + 0.020*\"camera\" + 0.008*\"fill\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"conclusão\" + 0.000*\"deputado\" + 0.000*\"declarado\"\n",
            "Topic: 1 \n",
            "Words: 0.394*\"learn\" + 0.324*\"machin\" + 0.103*\"american\" + 0.025*\"societi\" + 0.025*\"unknown\" + 0.022*\"repositori\" + 0.014*\"donat\" + 0.006*\"enjoy\" + 0.006*\"clearli\" + 0.003*\"leaflet\"\n",
            "Topic: 2 \n",
            "Words: 0.513*\"properti\" + 0.196*\"clean\" + 0.034*\"afford\" + 0.009*\"ladder\" + 0.003*\"rise\" + 0.000*\"conclusão\" + 0.000*\"característica\" + 0.000*\"declarado\" + 0.000*\"doaçõ\" + 0.000*\"deputado\"\n",
            "Topic: 3 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 4 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 5 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 6 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 7 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 8 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 9 \n",
            "Words: 0.673*\"review\" + 0.062*\"rate\" + 0.034*\"amazon\" + 0.016*\"product\" + 0.003*\"reviewscsv\" + 0.000*\"deputado\" + 0.000*\"criado\" + 0.000*\"característica\" + 0.000*\"declarado\" + 0.000*\"doaçõ\"\n",
            "Topic: 10 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 11 \n",
            "Words: 0.331*\"monthli\" + 0.089*\"scraper\" + 0.044*\"doctor\" + 0.000*\"conclusão\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"deputado\" + 0.000*\"criado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 12 \n",
            "Words: 0.507*\"other\" + 0.077*\"minut\" + 0.071*\"particip\" + 0.070*\"accur\" + 0.066*\"prefer\" + 0.034*\"professor\" + 0.021*\"valuabl\" + 0.021*\"opposit\" + 0.014*\"columbia\" + 0.009*\"racial\"\n",
            "Topic: 13 \n",
            "Words: 0.411*\"digit\" + 0.116*\"run\" + 0.060*\"instrument\" + 0.040*\"protocol\" + 0.030*\"fourth\" + 0.030*\"grayscal\" + 0.022*\"grate\" + 0.008*\"zalando\" + 0.006*\"cloth\" + 0.000*\"doaçõ\"\n",
            "Topic: 14 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 15 \n",
            "Words: 0.213*\"movi\" + 0.145*\"recommend\" + 0.123*\"filter\" + 0.102*\"thought\" + 0.091*\"pictur\" + 0.056*\"collabor\" + 0.033*\"organis\" + 0.031*\"movielen\" + 0.022*\"grouplen\" + 0.007*\"poster\"\n",
            "Topic: 16 \n",
            "Words: 0.310*\"consist\" + 0.138*\"minim\" + 0.073*\"durat\" + 0.068*\"appear\" + 0.067*\"continu\" + 0.059*\"find\" + 0.059*\"certain\" + 0.046*\"post\" + 0.042*\"independ\" + 0.029*\"rather\"\n",
            "Topic: 17 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 18 \n",
            "Words: 0.555*\"point\" + 0.171*\"varieti\" + 0.000*\"característica\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"robotsthi\" + 0.000*\"deputado\" + 0.000*\"declarado\" + 0.000*\"conclusão\"\n",
            "Topic: 19 \n",
            "Words: 0.283*\"sentenc\" + 0.271*\"account\" + 0.055*\"favorit\" + 0.049*\"divers\" + 0.044*\"intent\" + 0.031*\"attract\" + 0.025*\"amongst\" + 0.015*\"thoroughli\" + 0.008*\"drop\" + 0.004*\"audiovideo\"\n",
            "Topic: 20 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 21 \n",
            "Words: 0.379*\"system\" + 0.186*\"algorithm\" + 0.107*\"search\" + 0.074*\"written\" + 0.025*\"behaviour\" + 0.023*\"perspect\" + 0.023*\"write\" + 0.020*\"surround\" + 0.015*\"reveal\" + 0.012*\"style\"\n",
            "Topic: 22 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 23 \n",
            "Words: 0.339*\"model\" + 0.228*\"evalu\" + 0.161*\"simpl\" + 0.117*\"regress\" + 0.016*\"seattl\" + 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"declarado\" + 0.000*\"característica\" + 0.000*\"deputado\"\n",
            "Topic: 24 \n",
            "Words: 0.286*\"sampl\" + 0.239*\"measur\" + 0.110*\"calcul\" + 0.063*\"random\" + 0.027*\"telecommun\" + 0.024*\"forest\" + 0.024*\"highest\" + 0.024*\"baselin\" + 0.021*\"centroid\" + 0.018*\"absolut\"\n",
            "Topic: 25 \n",
            "Words: 0.375*\"statist\" + 0.198*\"score\" + 0.067*\"award\" + 0.039*\"basebal\" + 0.035*\"ground\" + 0.033*\"stand\" + 0.027*\"pull\" + 0.026*\"bat\" + 0.024*\"pitch\" + 0.020*\"postseason\"\n",
            "Topic: 26 \n",
            "Words: 0.239*\"fund\" + 0.232*\"mobil\" + 0.000*\"declarado\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"conclusão\" + 0.000*\"agradeço\" + 0.000*\"criado\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\"\n",
            "Topic: 27 \n",
            "Words: 0.120*\"wikipedia\" + 0.116*\"befor\" + 0.072*\"exist\" + 0.064*\"correct\" + 0.062*\"registr\" + 0.059*\"figur\" + 0.057*\"someth\" + 0.048*\"stanford\" + 0.044*\"get\" + 0.033*\"longer\"\n",
            "Topic: 28 \n",
            "Words: 0.278*\"imag\" + 0.128*\"comput\" + 0.099*\"pretrain\" + 0.090*\"label\" + 0.083*\"curat\" + 0.055*\"learn\" + 0.046*\"recognit\" + 0.042*\"manual\" + 0.025*\"mention\" + 0.022*\"vision\"\n",
            "Topic: 29 \n",
            "Words: 0.234*\"against\" + 0.232*\"pokemon\" + 0.183*\"damag\" + 0.032*\"resist\" + 0.020*\"special\" + 0.003*\"scratch\" + 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"candidato\" + 0.000*\"declarado\"\n",
            "Topic: 30 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 31 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 32 \n",
            "Words: 0.593*\"dure\" + 0.082*\"flight\" + 0.016*\"fli\" + 0.014*\"mountain\" + 0.000*\"conclusão\" + 0.000*\"característica\" + 0.000*\"candidato\" + 0.000*\"criado\" + 0.000*\"disponibilizar\" + 0.000*\"declarado\"\n",
            "Topic: 33 \n",
            "Words: 0.306*\"gener\" + 0.278*\"metadata\" + 0.052*\"subset\" + 0.051*\"energi\" + 0.051*\"investig\" + 0.048*\"help\" + 0.035*\"commiss\" + 0.019*\"corpor\" + 0.016*\"notebook\" + 0.013*\"launch\"\n",
            "Topic: 34 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 35 \n",
            "Words: 0.138*\"mathemat\" + 0.111*\"spend\" + 0.085*\"economi\" + 0.059*\"strongli\" + 0.051*\"quickli\" + 0.037*\"western\" + 0.033*\"interfac\" + 0.031*\"borrow\" + 0.031*\"brand\" + 0.027*\"centr\"\n",
            "Topic: 36 \n",
            "Words: 0.163*\"analyst\" + 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"agradeço\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 37 \n",
            "Words: 0.217*\"integ\" + 0.175*\"categor\" + 0.104*\"decis\" + 0.087*\"alway\" + 0.055*\"interest\" + 0.051*\"environ\" + 0.027*\"habit\" + 0.025*\"outlier\" + 0.020*\"see\" + 0.018*\"villag\"\n",
            "Topic: 38 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 39 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 40 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 41 \n",
            "Words: 0.428*\"acquir\" + 0.230*\"object\" + 0.077*\"expens\" + 0.049*\"therefor\" + 0.019*\"fail\" + 0.010*\"rare\" + 0.009*\"dividend\" + 0.007*\"usbas\" + 0.006*\"highqual\" + 0.003*\"nasdaq\"\n",
            "Topic: 42 \n",
            "Words: 0.324*\"citat\" + 0.204*\"activ\" + 0.067*\"decemb\" + 0.067*\"journal\" + 0.063*\"success\" + 0.043*\"effici\" + 0.037*\"live\" + 0.033*\"carri\" + 0.015*\"walk\" + 0.011*\"overlap\"\n",
            "Topic: 43 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 44 \n",
            "Words: 0.567*\"output\" + 0.023*\"championship\" + 0.000*\"característica\" + 0.000*\"agradeço\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"robotsthi\" + 0.000*\"deputado\" + 0.000*\"declarado\" + 0.000*\"conclusão\"\n",
            "Topic: 45 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 46 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 47 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 48 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 49 \n",
            "Words: 0.089*\"copyright\" + 0.076*\"softwar\" + 0.074*\"limit\" + 0.058*\"class\" + 0.056*\"test\" + 0.055*\"permiss\" + 0.044*\"modifi\" + 0.036*\"notic\" + 0.034*\"grant\" + 0.032*\"permit\"\n",
            "Topic: 50 \n",
            "Words: 0.283*\"charact\" + 0.283*\"seri\" + 0.077*\"final\" + 0.060*\"comprehens\" + 0.049*\"martin\" + 0.023*\"georg\" + 0.006*\"landscap\" + 0.001*\"battlescsv\" + 0.001*\"albon\" + 0.000*\"felip\"\n",
            "Topic: 51 \n",
            "Words: 0.575*\"factor\" + 0.077*\"particularli\" + 0.000*\"conclusão\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"criado\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"robotsthi\" + 0.000*\"declarado\"\n",
            "Topic: 52 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 53 \n",
            "Words: 0.468*\"request\" + 0.140*\"januari\" + 0.135*\"septemb\" + 0.049*\"friend\" + 0.000*\"criado\" + 0.000*\"deputado\" + 0.000*\"conclusão\" + 0.000*\"candidato\" + 0.000*\"eleito\" + 0.000*\"doaçõ\"\n",
            "Topic: 54 \n",
            "Words: 0.008*\"productnam\" + 0.000*\"conclusão\" + 0.000*\"declarado\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"agradeço\" + 0.000*\"criado\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"disponibilizar\"\n",
            "Topic: 55 \n",
            "Words: 0.102*\"timestamp\" + 0.071*\"signific\" + 0.059*\"symbol\" + 0.048*\"handl\" + 0.048*\"accept\" + 0.047*\"spread\" + 0.042*\"contract\" + 0.040*\"registri\" + 0.039*\"plan\" + 0.038*\"crypto\"\n",
            "Topic: 56 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 57 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 58 \n",
            "Words: 0.094*\"harvest\" + 0.000*\"conclusão\" + 0.000*\"declarado\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"agradeço\" + 0.000*\"criado\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"disponibilizar\"\n",
            "Topic: 59 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 60 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 61 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 62 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 63 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 64 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 65 \n",
            "Words: 0.271*\"estim\" + 0.146*\"subject\" + 0.101*\"censu\" + 0.080*\"count\" + 0.074*\"defin\" + 0.056*\"interact\" + 0.041*\"session\" + 0.035*\"mechan\" + 0.032*\"jurisdict\" + 0.016*\"zillow\"\n",
            "Topic: 66 \n",
            "Words: 0.350*\"govern\" + 0.264*\"feder\" + 0.128*\"polit\" + 0.038*\"affair\" + 0.033*\"affili\" + 0.003*\"redact\" + 0.000*\"disponibilizar\" + 0.000*\"conclusão\" + 0.000*\"característica\" + 0.000*\"agradeço\"\n",
            "Topic: 67 \n",
            "Words: 0.249*\"present\" + 0.215*\"share\" + 0.214*\"obtain\" + 0.097*\"adjust\" + 0.068*\"belong\" + 0.000*\"conclusão\" + 0.000*\"característica\" + 0.000*\"candidato\" + 0.000*\"criado\" + 0.000*\"disponibilizar\"\n",
            "Topic: 68 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 69 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 70 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 71 \n",
            "Words: 0.260*\"vehicl\" + 0.059*\"right\" + 0.052*\"strength\" + 0.049*\"michael\" + 0.049*\"almost\" + 0.049*\"premier\" + 0.043*\"kill\" + 0.041*\"penalti\" + 0.034*\"partner\" + 0.031*\"fantasi\"\n",
            "Topic: 72 \n",
            "Words: 0.432*\"separ\" + 0.168*\"speci\" + 0.061*\"classic\" + 0.021*\"taxonom\" + 0.014*\"flower\" + 0.013*\"firstli\" + 0.011*\"linearli\" + 0.003*\"secondli\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\"\n",
            "Topic: 73 \n",
            "Words: 0.681*\"repres\" + 0.127*\"transfer\" + 0.055*\"behavior\" + 0.012*\"massiv\" + 0.000*\"criado\" + 0.000*\"declarado\" + 0.000*\"característica\" + 0.000*\"deputado\" + 0.000*\"eleito\" + 0.000*\"doaçõ\"\n",
            "Topic: 74 \n",
            "Words: 0.393*\"largest\" + 0.067*\"close\" + 0.054*\"optim\" + 0.043*\"come\" + 0.039*\"profit\" + 0.038*\"correctli\" + 0.037*\"dimens\" + 0.033*\"portion\" + 0.024*\"benign\" + 0.019*\"malign\"\n",
            "Topic: 75 \n",
            "Words: 0.494*\"languag\" + 0.220*\"english\" + 0.050*\"transcript\" + 0.036*\"topic\" + 0.031*\"speaker\" + 0.027*\"chapter\" + 0.027*\"debat\" + 0.007*\"film\" + 0.006*\"fascin\" + 0.000*\"disponibilizar\"\n",
            "Topic: 76 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 77 \n",
            "Words: 0.129*\"california\" + 0.101*\"bureau\" + 0.092*\"consum\" + 0.090*\"transport\" + 0.090*\"summari\" + 0.072*\"travel\" + 0.057*\"statement\" + 0.052*\"client\" + 0.047*\"payment\" + 0.026*\"default\"\n",
            "Topic: 78 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 79 \n",
            "Words: 0.370*\"player\" + 0.106*\"event\" + 0.096*\"leagu\" + 0.067*\"play\" + 0.052*\"assist\" + 0.050*\"integr\" + 0.039*\"footbal\" + 0.023*\"pass\" + 0.022*\"chanc\" + 0.020*\"soccer\"\n",
            "Topic: 80 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 81 \n",
            "Words: 0.153*\"solut\" + 0.131*\"display\" + 0.119*\"take\" + 0.062*\"summar\" + 0.044*\"land\" + 0.040*\"encount\" + 0.030*\"punctuat\" + 0.021*\"crash\" + 0.020*\"chart\" + 0.010*\"aboard\"\n",
            "Topic: 82 \n",
            "Words: 0.131*\"support\" + 0.088*\"vector\" + 0.076*\"sensor\" + 0.073*\"proceed\" + 0.059*\"captur\" + 0.052*\"embed\" + 0.037*\"intellig\" + 0.031*\"window\" + 0.025*\"workshop\" + 0.024*\"artifici\"\n",
            "Topic: 83 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 84 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 85 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 86 \n",
            "Words: 0.254*\"archiv\" + 0.199*\"letter\" + 0.160*\"valid\" + 0.074*\"scientif\" + 0.022*\"feel\" + 0.000*\"conclusão\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"agradeço\" + 0.000*\"criado\"\n",
            "Topic: 87 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 88 \n",
            "Words: 0.436*\"featur\" + 0.243*\"valu\" + 0.125*\"miss\" + 0.032*\"server\" + 0.026*\"signific\" + 0.009*\"mangasarian\" + 0.006*\"contour\" + 0.005*\"perimet\" + 0.005*\"semest\" + 0.005*\"textur\"\n",
            "Topic: 89 \n",
            "Words: 0.407*\"indic\" + 0.382*\"compani\" + 0.046*\"yearli\" + 0.000*\"característica\" + 0.000*\"deputado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"declarado\" + 0.000*\"conclusão\" + 0.000*\"criado\"\n",
            "Topic: 90 \n",
            "Words: 0.484*\"engin\" + 0.014*\"append\" + 0.000*\"conclusão\" + 0.000*\"declarado\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"agradeço\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"criado\"\n",
            "Topic: 91 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 92 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 93 \n",
            "Words: 0.291*\"inventori\" + 0.141*\"profession\" + 0.000*\"característica\" + 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"declarado\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"doaçõ\"\n",
            "Topic: 94 \n",
            "Words: 0.284*\"establish\" + 0.140*\"aspect\" + 0.019*\"patch\" + 0.000*\"robotsthi\" + 0.000*\"antun\" + 0.000*\"disgust\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"conclusão\" + 0.000*\"característica\" + 0.000*\"agradeço\"\n",
            "Topic: 95 \n",
            "Words: 0.303*\"locat\" + 0.107*\"coordin\" + 0.096*\"polic\" + 0.084*\"latitud\" + 0.083*\"longitud\" + 0.071*\"administr\" + 0.033*\"portal\" + 0.032*\"concern\" + 0.019*\"park\" + 0.018*\"complaint\"\n",
            "Topic: 96 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 97 \n",
            "Words: 0.255*\"attack\" + 0.241*\"defens\" + 0.099*\"denot\" + 0.051*\"teach\" + 0.046*\"infer\" + 0.024*\"plot\" + 0.018*\"opensourc\" + 0.017*\"terrorist\" + 0.016*\"bubbl\" + 0.007*\"worthi\"\n",
            "Topic: 98 \n",
            "Words: 0.264*\"categori\" + 0.129*\"either\" + 0.119*\"normal\" + 0.076*\"length\" + 0.062*\"situat\" + 0.052*\"background\" + 0.043*\"overview\" + 0.036*\"consider\" + 0.032*\"distinguish\" + 0.027*\"equal\"\n",
            "Topic: 99 \n",
            "Words: 0.201*\"there\" + 0.127*\"social\" + 0.091*\"accord\" + 0.090*\"recent\" + 0.033*\"bias\" + 0.032*\"facebook\" + 0.030*\"daniel\" + 0.029*\"choic\" + 0.028*\"disclaim\" + 0.025*\"choos\"\n",
            "Topic: 100 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 101 \n",
            "Words: 0.525*\"question\" + 0.383*\"answer\" + 0.004*\"ask\" + 0.004*\"choic\" + 0.000*\"conclusão\" + 0.000*\"característica\" + 0.000*\"candidato\" + 0.000*\"criado\" + 0.000*\"agradeço\" + 0.000*\"declarado\"\n",
            "Topic: 102 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 103 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 104 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 105 \n",
            "Words: 0.386*\"develop\" + 0.090*\"annual\" + 0.072*\"borough\" + 0.061*\"protect\" + 0.053*\"driver\" + 0.049*\"vari\" + 0.046*\"hundr\" + 0.042*\"essenti\" + 0.032*\"lead\" + 0.014*\"manhattan\"\n",
            "Topic: 106 \n",
            "Words: 0.278*\"region\" + 0.231*\"street\" + 0.127*\"respect\" + 0.091*\"degre\" + 0.067*\"growth\" + 0.049*\"attend\" + 0.008*\"northeast\" + 0.005*\"midwest\" + 0.000*\"disponibilizar\" + 0.000*\"característica\"\n",
            "Topic: 107 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 108 \n",
            "Words: 0.596*\"author\" + 0.082*\"token\" + 0.038*\"compat\" + 0.000*\"característica\" + 0.000*\"conclusão\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"deputado\" + 0.000*\"criado\" + 0.000*\"declarado\"\n",
            "Topic: 109 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 110 \n",
            "Words: 0.269*\"instanc\" + 0.223*\"employ\" + 0.114*\"foreign\" + 0.060*\"certif\" + 0.050*\"worker\" + 0.029*\"immigr\" + 0.026*\"petit\" + 0.012*\"fulltim\" + 0.000*\"criado\" + 0.000*\"conclusão\"\n",
            "Topic: 111 \n",
            "Words: 0.446*\"field\" + 0.118*\"deriv\" + 0.093*\"enough\" + 0.074*\"divis\" + 0.026*\"doesnt\" + 0.016*\"behav\" + 0.013*\"shorter\" + 0.009*\"portfolio\" + 0.005*\"roll\" + 0.004*\"undervalu\"\n",
            "Topic: 112 \n",
            "Words: 0.169*\"method\" + 0.138*\"sever\" + 0.135*\"challeng\" + 0.113*\"detect\" + 0.074*\"automat\" + 0.039*\"reduc\" + 0.029*\"feedback\" + 0.028*\"hospit\" + 0.026*\"precis\" + 0.025*\"compon\"\n",
            "Topic: 113 \n",
            "Words: 0.453*\"gender\" + 0.220*\"femal\" + 0.009*\"quantil\" + 0.000*\"declarado\" + 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"característica\" + 0.000*\"deputado\" + 0.000*\"eleito\" + 0.000*\"disponibilizar\"\n",
            "Topic: 114 \n",
            "Words: 0.473*\"thoma\" + 0.137*\"weapon\" + 0.110*\"justic\" + 0.000*\"agradeço\" + 0.000*\"antun\" + 0.000*\"robotsthi\" + 0.000*\"candidato\" + 0.000*\"criado\" + 0.000*\"conclusão\" + 0.000*\"declarado\"\n",
            "Topic: 115 \n",
            "Words: 0.191*\"elect\" + 0.168*\"candid\" + 0.160*\"primari\" + 0.084*\"shapefil\" + 0.043*\"vote\" + 0.041*\"discov\" + 0.039*\"presidenti\" + 0.023*\"result\" + 0.022*\"moment\" + 0.017*\"uptod\"\n",
            "Topic: 116 \n",
            "Words: 0.454*\"attribut\" + 0.325*\"variabl\" + 0.047*\"comparison\" + 0.028*\"identif\" + 0.019*\"ad\" + 0.014*\"ultim\" + 0.013*\"asset\" + 0.010*\"greater\" + 0.006*\"exist\" + 0.005*\"appoint\"\n",
            "Topic: 117 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 118 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 119 \n",
            "Words: 0.256*\"list\" + 0.146*\"comment\" + 0.079*\"smaller\" + 0.069*\"canada\" + 0.053*\"grade\" + 0.052*\"youtub\" + 0.043*\"video\" + 0.025*\"magazin\" + 0.022*\"germani\" + 0.019*\"britain\"\n",
            "Topic: 120 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 121 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 122 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 123 \n",
            "Words: 0.327*\"scrape\" + 0.288*\"million\" + 0.075*\"platform\" + 0.039*\"america\" + 0.032*\"europ\" + 0.020*\"greater\" + 0.018*\"beautifulsoup\" + 0.016*\"incomplet\" + 0.011*\"subscrib\" + 0.008*\"metacrit\"\n",
            "Topic: 124 \n",
            "Words: 0.294*\"requir\" + 0.285*\"qualiti\" + 0.173*\"restaur\" + 0.019*\"nutrit\" + 0.013*\"tast\" + 0.008*\"watch\" + 0.002*\"intak\" + 0.000*\"característica\" + 0.000*\"declarado\" + 0.000*\"conclusão\"\n",
            "Topic: 125 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 126 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 127 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 128 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 129 \n",
            "Words: 0.465*\"access\" + 0.259*\"standard\" + 0.055*\"territori\" + 0.045*\"schema\" + 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"característica\" + 0.000*\"disponibilizar\" + 0.000*\"doaçõ\" + 0.000*\"declarado\"\n",
            "Topic: 130 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 131 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 132 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 133 \n",
            "Words: 0.136*\"percent\" + 0.099*\"screen\" + 0.086*\"have\" + 0.080*\"rel\" + 0.073*\"studi\" + 0.051*\"poverti\" + 0.048*\"local\" + 0.045*\"prevent\" + 0.043*\"kindli\" + 0.033*\"examin\"\n",
            "Topic: 134 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 135 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 136 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 137 \n",
            "Words: 0.711*\"licens\" + 0.026*\"overflow\" + 0.000*\"criado\" + 0.000*\"característica\" + 0.000*\"conclusão\" + 0.000*\"candidato\" + 0.000*\"doaçõ\" + 0.000*\"disponibilizar\" + 0.000*\"agradeço\" + 0.000*\"deputado\"\n",
            "Topic: 138 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 139 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 140 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 141 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 142 \n",
            "Words: 0.320*\"season\" + 0.157*\"involv\" + 0.126*\"analyt\" + 0.121*\"sport\" + 0.031*\"england\" + 0.029*\"invit\" + 0.022*\"versu\" + 0.000*\"criado\" + 0.000*\"declarado\" + 0.000*\"característica\"\n",
            "Topic: 143 \n",
            "Words: 0.254*\"complet\" + 0.137*\"dictionari\" + 0.130*\"issu\" + 0.114*\"matrix\" + 0.061*\"thousand\" + 0.052*\"latest\" + 0.040*\"nearest\" + 0.026*\"defend\" + 0.022*\"quarter\" + 0.005*\"hypothesi\"\n",
            "Topic: 144 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 145 \n",
            "Words: 0.179*\"german\" + 0.081*\"seller\" + 0.077*\"brought\" + 0.036*\"crawler\" + 0.029*\"dealer\" + 0.025*\"kilomet\" + 0.019*\"scrapi\" + 0.004*\"everywher\" + 0.004*\"datecr\" + 0.002*\"datecrawl\"\n",
            "Topic: 146 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 147 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 148 \n",
            "Words: 0.261*\"cluster\" + 0.200*\"regist\" + 0.063*\"aircraft\" + 0.060*\"throughout\" + 0.057*\"termin\" + 0.056*\"mainli\" + 0.054*\"welcom\" + 0.020*\"altitud\" + 0.015*\"plane\" + 0.010*\"airplan\"\n",
            "Topic: 149 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 150 \n",
            "Words: 0.349*\"research\" + 0.185*\"start\" + 0.175*\"without\" + 0.139*\"world\" + 0.050*\"match\" + 0.042*\"place\" + 0.010*\"everyth\" + 0.000*\"antun\" + 0.000*\"agradeço\" + 0.000*\"candidato\"\n",
            "Topic: 151 \n",
            "Words: 0.234*\"citi\" + 0.126*\"octob\" + 0.090*\"novemb\" + 0.087*\"safeti\" + 0.074*\"fatal\" + 0.066*\"shoot\" + 0.036*\"attent\" + 0.034*\"extern\" + 0.021*\"audienc\" + 0.000*\"disponibilizar\"\n",
            "Topic: 152 \n",
            "Words: 0.127*\"histor\" + 0.105*\"price\" + 0.096*\"affect\" + 0.077*\"paramet\" + 0.072*\"currenc\" + 0.071*\"exchang\" + 0.069*\"transact\" + 0.059*\"exclud\" + 0.044*\"capit\" + 0.036*\"attach\"\n",
            "Topic: 153 \n",
            "Words: 0.582*\"project\" + 0.201*\"explanatori\" + 0.050*\"convers\" + 0.016*\"enthusiast\" + 0.011*\"kickstart\" + 0.005*\"mileston\" + 0.005*\"success\" + 0.002*\"crowdfund\" + 0.001*\"pledg\" + 0.000*\"declarado\"\n",
            "Topic: 154 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 155 \n",
            "Words: 0.169*\"consid\" + 0.151*\"experi\" + 0.130*\"percentag\" + 0.086*\"resid\" + 0.066*\"overal\" + 0.041*\"opinion\" + 0.038*\"forward\" + 0.035*\"appreci\" + 0.028*\"flavor\" + 0.014*\"impress\"\n",
            "Topic: 156 \n",
            "Words: 0.293*\"counti\" + 0.253*\"kernel\" + 0.188*\"traffic\" + 0.051*\"emerg\" + 0.008*\"montgomeri\" + 0.001*\"introduct\" + 0.000*\"declarado\" + 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"candidato\"\n",
            "Topic: 157 \n",
            "Words: 0.363*\"period\" + 0.156*\"product\" + 0.116*\"target\" + 0.068*\"month\" + 0.048*\"forecast\" + 0.035*\"tri\" + 0.032*\"weekli\" + 0.023*\"quantiti\" + 0.022*\"suppli\" + 0.012*\"snapshot\"\n",
            "Topic: 158 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 159 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 160 \n",
            "Words: 0.261*\"employe\" + 0.189*\"salari\" + 0.159*\"explain\" + 0.018*\"happi\" + 0.000*\"deputado\" + 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"declarado\" + 0.000*\"candidato\" + 0.000*\"disponibilizar\"\n",
            "Topic: 161 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 162 \n",
            "Words: 0.821*\"concept\" + 0.052*\"metric\" + 0.016*\"kaggler\" + 0.015*\"upvot\" + 0.004*\"rememb\" + 0.003*\"bottom\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"característica\" + 0.000*\"candidato\"\n",
            "Topic: 163 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 164 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 165 \n",
            "Words: 0.472*\"between\" + 0.111*\"across\" + 0.064*\"group\" + 0.037*\"track\" + 0.037*\"benefit\" + 0.031*\"washington\" + 0.029*\"francisco\" + 0.026*\"budget\" + 0.022*\"encourag\" + 0.017*\"constitut\"\n",
            "Topic: 166 \n",
            "Words: 0.265*\"market\" + 0.186*\"volum\" + 0.151*\"further\" + 0.065*\"trade\" + 0.065*\"thing\" + 0.051*\"financ\" + 0.042*\"stock\" + 0.020*\"move\" + 0.013*\"switch\" + 0.009*\"reach\"\n",
            "Topic: 167 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 168 \n",
            "Words: 0.233*\"frequenc\" + 0.199*\"speech\" + 0.144*\"signal\" + 0.061*\"recognit\" + 0.040*\"carnegi\" + 0.037*\"mellon\" + 0.021*\"spectral\" + 0.014*\"voic\" + 0.011*\"xgboost\" + 0.011*\"incorrectli\"\n",
            "Topic: 169 \n",
            "Words: 0.306*\"peopl\" + 0.239*\"person\" + 0.105*\"becaus\" + 0.095*\"busi\" + 0.056*\"divid\" + 0.056*\"suggest\" + 0.019*\"distinct\" + 0.016*\"judg\" + 0.012*\"reason\" + 0.005*\"thing\"\n",
            "Topic: 170 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 171 \n",
            "Words: 0.312*\"classif\" + 0.242*\"classifi\" + 0.153*\"achiev\" + 0.068*\"artist\" + 0.033*\"slightli\" + 0.020*\"quantifi\" + 0.013*\"lyric\" + 0.010*\"selfexplanatori\" + 0.002*\"inconveni\" + 0.000*\"disponibilizar\"\n",
            "Topic: 172 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 173 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 174 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 175 \n",
            "Words: 0.311*\"record\" + 0.243*\"nation\" + 0.124*\"organ\" + 0.069*\"cover\" + 0.050*\"map\" + 0.048*\"prepar\" + 0.026*\"tutori\" + 0.024*\"elev\" + 0.012*\"introduc\" + 0.010*\"conclus\"\n",
            "Topic: 176 \n",
            "Words: 0.134*\"manag\" + 0.116*\"agenc\" + 0.090*\"incid\" + 0.088*\"occur\" + 0.061*\"except\" + 0.048*\"secur\" + 0.045*\"reserv\" + 0.037*\"offens\" + 0.034*\"crimin\" + 0.031*\"director\"\n",
            "Topic: 177 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 178 \n",
            "Words: 0.277*\"intern\" + 0.176*\"applic\" + 0.107*\"relev\" + 0.102*\"mine\" + 0.093*\"confer\" + 0.087*\"characterist\" + 0.032*\"open\" + 0.001*\"multiag\" + 0.000*\"conclusão\" + 0.000*\"doaçõ\"\n",
            "Topic: 179 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 180 \n",
            "Words: 0.192*\"death\" + 0.185*\"failur\" + 0.166*\"surviv\" + 0.000*\"criado\" + 0.000*\"deputado\" + 0.000*\"característica\" + 0.000*\"conclusão\" + 0.000*\"antun\" + 0.000*\"doaçõ\" + 0.000*\"disponibilizar\"\n",
            "Topic: 181 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 182 \n",
            "Words: 0.438*\"commun\" + 0.329*\"scienc\" + 0.055*\"tabl\" + 0.051*\"publicli\" + 0.019*\"win\" + 0.018*\"sqlite\" + 0.009*\"enrich\" + 0.007*\"entertain\" + 0.003*\"strateg\" + 0.001*\"say\"\n",
            "Topic: 183 \n",
            "Words: 0.169*\"inform\" + 0.100*\"avail\" + 0.100*\"sourc\" + 0.069*\"creat\" + 0.062*\"analysi\" + 0.052*\"detail\" + 0.047*\"pleas\" + 0.043*\"interest\" + 0.036*\"relat\" + 0.031*\"current\"\n",
            "Topic: 184 \n",
            "Words: 0.114*\"oper\" + 0.111*\"contribut\" + 0.102*\"increas\" + 0.097*\"allow\" + 0.087*\"expect\" + 0.083*\"level\" + 0.082*\"residu\" + 0.080*\"approxim\" + 0.072*\"trend\" + 0.023*\"privaci\"\n",
            "Topic: 185 \n",
            "Words: 0.175*\"want\" + 0.093*\"balanc\" + 0.083*\"order\" + 0.067*\"instead\" + 0.063*\"african\" + 0.042*\"recipi\" + 0.040*\"famou\" + 0.033*\"thesi\" + 0.028*\"supplier\" + 0.025*\"alabama\"\n",
            "Topic: 186 \n",
            "Words: 0.373*\"packag\" + 0.200*\"around\" + 0.146*\"resourc\" + 0.144*\"github\" + 0.008*\"expans\" + 0.000*\"candidato\" + 0.000*\"deputado\" + 0.000*\"declarado\" + 0.000*\"característica\" + 0.000*\"criado\"\n",
            "Topic: 187 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 188 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 189 \n",
            "Words: 0.237*\"contact\" + 0.223*\"binari\" + 0.093*\"reddit\" + 0.091*\"directli\" + 0.068*\"channel\" + 0.048*\"crawl\" + 0.010*\"stay\" + 0.006*\"friendli\" + 0.003*\"worldnew\" + 0.000*\"declarado\"\n",
            "Topic: 190 \n",
            "Words: 0.094*\"modul\" + 0.068*\"mysteri\" + 0.055*\"neighbourhood\" + 0.054*\"forget\" + 0.015*\"scholarship\" + 0.008*\"be\" + 0.004*\"lastli\" + 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"característica\"\n",
            "Topic: 191 \n",
            "Words: 0.299*\"statu\" + 0.246*\"occup\" + 0.110*\"submit\" + 0.062*\"visit\" + 0.027*\"absenc\" + 0.023*\"underli\" + 0.000*\"deputado\" + 0.000*\"criado\" + 0.000*\"eleito\" + 0.000*\"doaçõ\"\n",
            "Topic: 192 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 193 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 194 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 195 \n",
            "Words: 0.627*\"releas\" + 0.088*\"nearli\" + 0.000*\"característica\" + 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"declarado\" + 0.000*\"antun\" + 0.000*\"doaçõ\" + 0.000*\"disponibilizar\" + 0.000*\"robotsthi\"\n",
            "Topic: 196 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 197 \n",
            "Words: 0.174*\"header\" + 0.137*\"becom\" + 0.112*\"billion\" + 0.102*\"edit\" + 0.046*\"metropolitan\" + 0.036*\"subreddit\" + 0.028*\"uncompress\" + 0.026*\"explicit\" + 0.023*\"spring\" + 0.016*\"parentid\"\n",
            "Topic: 198 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 199 \n",
            "Words: 0.358*\"distribut\" + 0.182*\"weight\" + 0.165*\"select\" + 0.058*\"execut\" + 0.047*\"block\" + 0.032*\"serv\" + 0.019*\"longest\" + 0.010*\"adopt\" + 0.005*\"viewer\" + 0.002*\"blockchain\"\n",
            "Topic: 200 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 201 \n",
            "Words: 0.260*\"origin\" + 0.180*\"extract\" + 0.169*\"version\" + 0.133*\"perform\" + 0.072*\"credit\" + 0.039*\"analys\" + 0.026*\"european\" + 0.018*\"skill\" + 0.015*\"entri\" + 0.012*\"centuri\"\n",
            "Topic: 202 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 203 \n",
            "Words: 0.450*\"report\" + 0.157*\"center\" + 0.127*\"correl\" + 0.121*\"like\" + 0.008*\"sight\" + 0.003*\"erron\" + 0.000*\"ramintern\" + 0.000*\"disponibilizar\" + 0.000*\"declarado\" + 0.000*\"deputado\"\n",
            "Topic: 204 \n",
            "Words: 0.238*\"translat\" + 0.136*\"protein\" + 0.069*\"myself\" + 0.058*\"interv\" + 0.058*\"breast\" + 0.046*\"confid\" + 0.035*\"molecul\" + 0.030*\"turn\" + 0.019*\"ratio\" + 0.000*\"declarado\"\n",
            "Topic: 205 \n",
            "Words: 0.358*\"depart\" + 0.243*\"compil\" + 0.057*\"freedom\" + 0.045*\"ethnic\" + 0.045*\"victim\" + 0.029*\"found\" + 0.029*\"murder\" + 0.008*\"homicid\" + 0.005*\"perpetr\" + 0.001*\"hargrov\"\n",
            "Topic: 206 \n",
            "Words: 0.292*\"corpu\" + 0.253*\"natur\" + 0.099*\"remov\" + 0.065*\"mean\" + 0.058*\"read\" + 0.041*\"anonym\" + 0.025*\"expand\" + 0.019*\"concaten\" + 0.018*\"ident\" + 0.018*\"exactli\"\n",
            "Topic: 207 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 208 \n",
            "Words: 0.216*\"excel\" + 0.209*\"medium\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"conclusão\" + 0.000*\"robotsthi\" + 0.000*\"deputado\" + 0.000*\"declarado\" + 0.000*\"disgust\"\n",
            "Topic: 209 \n",
            "Words: 0.499*\"amount\" + 0.158*\"dollar\" + 0.063*\"predictor\" + 0.000*\"declarado\" + 0.000*\"característica\" + 0.000*\"conclusão\" + 0.000*\"antun\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"criado\"\n",
            "Topic: 210 \n",
            "Words: 0.195*\"includ\" + 0.085*\"provid\" + 0.082*\"databas\" + 0.079*\"public\" + 0.056*\"result\" + 0.055*\"chang\" + 0.043*\"individu\" + 0.040*\"document\" + 0.037*\"through\" + 0.037*\"import\"\n",
            "Topic: 211 \n",
            "Words: 0.200*\"proport\" + 0.110*\"uniform\" + 0.018*\"upgrad\" + 0.000*\"candidato\" + 0.000*\"conclusão\" + 0.000*\"antun\" + 0.000*\"deputado\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"declarado\"\n",
            "Topic: 212 \n",
            "Words: 0.168*\"work\" + 0.140*\"anoth\" + 0.137*\"aggreg\" + 0.132*\"appli\" + 0.093*\"techniqu\" + 0.059*\"simpli\" + 0.052*\"extrem\" + 0.031*\"motiv\" + 0.021*\"alongsid\" + 0.000*\"criado\"\n",
            "Topic: 213 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 214 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 215 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 216 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 217 \n",
            "Words: 0.289*\"effect\" + 0.158*\"option\" + 0.110*\"begin\" + 0.075*\"set\" + 0.047*\"pretti\" + 0.037*\"deni\" + 0.026*\"alter\" + 0.000*\"agradeço\" + 0.000*\"robotsthi\" + 0.000*\"candidato\"\n",
            "Topic: 218 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 219 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 220 \n",
            "Words: 0.051*\"miner\" + 0.000*\"conclusão\" + 0.000*\"declarado\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"agradeço\" + 0.000*\"criado\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"disponibilizar\"\n",
            "Topic: 221 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 222 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 223 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 224 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 225 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 226 \n",
            "Words: 0.211*\"segment\" + 0.204*\"instruct\" + 0.118*\"someon\" + 0.102*\"action\" + 0.083*\"dialogu\" + 0.033*\"broken\" + 0.026*\"episod\" + 0.017*\"suffici\" + 0.015*\"load\" + 0.014*\"speak\"\n",
            "Topic: 227 \n",
            "Words: 0.416*\"contain\" + 0.166*\"follow\" + 0.109*\"format\" + 0.092*\"exampl\" + 0.047*\"combin\" + 0.045*\"correspond\" + 0.034*\"actual\" + 0.031*\"howev\" + 0.014*\"titl\" + 0.007*\"readm\"\n",
            "Topic: 228 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 229 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 230 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 231 \n",
            "Words: 0.084*\"listen\" + 0.000*\"conclusão\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"agradeço\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 232 \n",
            "Words: 0.174*\"regular\" + 0.164*\"decad\" + 0.159*\"cancer\" + 0.020*\"cervic\" + 0.000*\"deputado\" + 0.000*\"característica\" + 0.000*\"conclusão\" + 0.000*\"antun\" + 0.000*\"criado\" + 0.000*\"doaçõ\"\n",
            "Topic: 233 \n",
            "Words: 0.159*\"indian\" + 0.151*\"diseas\" + 0.145*\"medic\" + 0.139*\"patient\" + 0.072*\"larger\" + 0.042*\"diabet\" + 0.010*\"heritag\" + 0.008*\"johann\" + 0.005*\"diagnost\" + 0.004*\"kidney\"\n",
            "Topic: 234 \n",
            "Words: 0.275*\"hous\" + 0.256*\"link\" + 0.000*\"candidato\" + 0.000*\"agradeço\" + 0.000*\"antun\" + 0.000*\"robotsthi\" + 0.000*\"característica\" + 0.000*\"deputado\" + 0.000*\"declarado\" + 0.000*\"disgust\"\n",
            "Topic: 235 \n",
            "Words: 0.720*\"column\" + 0.181*\"second\" + 0.014*\"spectrum\" + 0.002*\"datacsv\" + 0.001*\"ourselv\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"agradeço\" + 0.000*\"declarado\"\n",
            "Topic: 236 \n",
            "Words: 0.306*\"identifi\" + 0.180*\"uniqu\" + 0.124*\"rate\" + 0.083*\"anim\" + 0.077*\"member\" + 0.061*\"assign\" + 0.030*\"genr\" + 0.024*\"view\" + 0.013*\"randomli\" + 0.009*\"userid\"\n",
            "Topic: 237 \n",
            "Words: 0.275*\"weather\" + 0.270*\"station\" + 0.199*\"temperatur\" + 0.021*\"celsiu\" + 0.010*\"collat\" + 0.003*\"slice\" + 0.002*\"repackag\" + 0.001*\"technician\" + 0.000*\"conclusão\" + 0.000*\"criado\"\n",
            "Topic: 238 \n",
            "Words: 0.267*\"distanc\" + 0.146*\"scientist\" + 0.088*\"expert\" + 0.051*\"decreas\" + 0.049*\"breakdown\" + 0.037*\"corrupt\" + 0.033*\"outstand\" + 0.023*\"character\" + 0.022*\"fiction\" + 0.017*\"uncov\"\n",
            "Topic: 239 \n",
            "Words: 0.363*\"receiv\" + 0.123*\"violat\" + 0.111*\"privat\" + 0.047*\"ensur\" + 0.036*\"oppon\" + 0.023*\"secretari\" + 0.019*\"heavili\" + 0.018*\"biggest\" + 0.013*\"monday\" + 0.009*\"controversi\"\n",
            "Topic: 240 \n",
            "Words: 0.404*\"publish\" + 0.214*\"articl\" + 0.159*\"onlin\" + 0.037*\"evolut\" + 0.029*\"headlin\" + 0.018*\"rang\" + 0.007*\"advic\" + 0.004*\"expertis\" + 0.003*\"leskovec\" + 0.003*\"databasesqlit\"\n",
            "Topic: 241 \n",
            "Words: 0.479*\"typic\" + 0.035*\"proprietari\" + 0.011*\"familiar\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"agradeço\" + 0.000*\"antun\" + 0.000*\"criado\" + 0.000*\"conclusão\" + 0.000*\"declarado\"\n",
            "Topic: 242 \n",
            "Words: 0.537*\"countri\" + 0.116*\"econom\" + 0.075*\"polici\" + 0.070*\"famili\" + 0.029*\"capita\" + 0.017*\"social\" + 0.016*\"policymak\" + 0.011*\"extent\" + 0.007*\"contrast\" + 0.004*\"wellb\"\n",
            "Topic: 243 \n",
            "Words: 0.327*\"health\" + 0.172*\"condit\" + 0.093*\"physic\" + 0.049*\"interview\" + 0.030*\"primarili\" + 0.029*\"ill\" + 0.027*\"mental\" + 0.023*\"substanc\" + 0.022*\"treatment\" + 0.022*\"disord\"\n",
            "Topic: 244 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 245 \n",
            "Words: 0.258*\"kaggl\" + 0.208*\"colleg\" + 0.178*\"script\" + 0.131*\"cours\" + 0.036*\"host\" + 0.022*\"compress\" + 0.021*\"basic\" + 0.015*\"oblig\" + 0.009*\"scorecard\" + 0.006*\"understood\"\n",
            "Topic: 246 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 247 \n",
            "Words: 0.682*\"internet\" + 0.044*\"stori\" + 0.033*\"fivethirtyeight\" + 0.029*\"occasion\" + 0.027*\"pickup\" + 0.007*\"limousin\" + 0.005*\"finegrain\" + 0.004*\"locationid\" + 0.000*\"candidato\" + 0.000*\"antun\"\n",
            "Topic: 248 \n",
            "Words: 0.139*\"sound\" + 0.020*\"unlabel\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"conclusão\" + 0.000*\"agradeço\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\"\n",
            "Topic: 249 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 250 \n",
            "Words: 0.481*\"posit\" + 0.097*\"profil\" + 0.042*\"pressur\" + 0.032*\"africa\" + 0.031*\"liter\" + 0.029*\"homepag\" + 0.021*\"pick\" + 0.019*\"playground\" + 0.018*\"reli\" + 0.015*\"career\"\n",
            "Topic: 251 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 252 \n",
            "Words: 0.500*\"predict\" + 0.147*\"competit\" + 0.095*\"histori\" + 0.072*\"outcom\" + 0.039*\"strategi\" + 0.027*\"master\" + 0.024*\"winner\" + 0.003*\"munich\" + 0.001*\"headtohead\" + 0.000*\"antun\"\n",
            "Topic: 253 \n",
            "Words: 0.277*\"revenu\" + 0.185*\"actor\" + 0.022*\"runtim\" + 0.000*\"conclusão\" + 0.000*\"declarado\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"agradeço\" + 0.000*\"doaçõ\" + 0.000*\"disponibilizar\"\n",
            "Topic: 254 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 255 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 256 \n",
            "Words: 0.167*\"problem\" + 0.151*\"domain\" + 0.129*\"initi\" + 0.099*\"knowledg\" + 0.082*\"control\" + 0.071*\"approach\" + 0.050*\"implement\" + 0.038*\"foundat\" + 0.038*\"simul\" + 0.019*\"explan\"\n",
            "Topic: 257 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 258 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 259 \n",
            "Words: 0.263*\"servic\" + 0.128*\"librari\" + 0.108*\"sentiment\" + 0.104*\"tweet\" + 0.082*\"neg\" + 0.076*\"twitter\" + 0.039*\"contributor\" + 0.028*\"everyon\" + 0.023*\"neutral\" + 0.022*\"airlin\"\n",
            "Topic: 260 \n",
            "Words: 0.598*\"numer\" + 0.056*\"ingredi\" + 0.021*\"android\" + 0.010*\"imageurl\" + 0.009*\"barcod\" + 0.003*\"allergen\" + 0.002*\"tidbit\" + 0.000*\"doaçõ\" + 0.000*\"deputado\" + 0.000*\"criado\"\n",
            "Topic: 261 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 262 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 263 \n",
            "Words: 0.469*\"opportun\" + 0.085*\"anyon\" + 0.072*\"offer\" + 0.071*\"though\" + 0.065*\"duplic\" + 0.020*\"theoret\" + 0.011*\"journey\" + 0.010*\"valley\" + 0.000*\"criado\" + 0.000*\"declarado\"\n",
            "Topic: 264 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 265 \n",
            "Words: 0.113*\"nonprofit\" + 0.031*\"trace\" + 0.026*\"reus\" + 0.021*\"checkout\" + 0.000*\"característica\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"agradeço\" + 0.000*\"conclusão\" + 0.000*\"deputado\"\n",
            "Topic: 266 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 267 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 268 \n",
            "Words: 0.204*\"remain\" + 0.154*\"height\" + 0.133*\"intend\" + 0.103*\"charg\" + 0.061*\"copi\" + 0.040*\"deal\" + 0.000*\"deputado\" + 0.000*\"disponibilizar\" + 0.000*\"doaçõ\" + 0.000*\"eleiçõ\"\n",
            "Topic: 269 \n",
            "Words: 0.276*\"websit\" + 0.193*\"common\" + 0.135*\"associ\" + 0.108*\"offici\" + 0.098*\"popular\" + 0.050*\"creativ\" + 0.046*\"mostli\" + 0.002*\"theme\" + 0.001*\"immens\" + 0.000*\"declarado\"\n",
            "Topic: 270 \n",
            "Words: 0.258*\"accuraci\" + 0.113*\"highli\" + 0.106*\"transform\" + 0.081*\"otherwis\" + 0.071*\"unfortun\" + 0.064*\"andrea\" + 0.040*\"princip\" + 0.014*\"confidenti\" + 0.012*\"unbalanc\" + 0.011*\"meaning\"\n",
            "Topic: 271 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 272 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 273 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 274 \n",
            "Words: 0.617*\"download\" + 0.096*\"mortal\" + 0.073*\"median\" + 0.000*\"agradeço\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"robotsthi\" + 0.000*\"característica\" + 0.000*\"declarado\" + 0.000*\"criado\"\n",
            "Topic: 275 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 276 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 277 \n",
            "Words: 0.351*\"googl\" + 0.208*\"folder\" + 0.058*\"ticker\" + 0.000*\"conclusão\" + 0.000*\"declarado\" + 0.000*\"característica\" + 0.000*\"antun\" + 0.000*\"doaçõ\" + 0.000*\"disponibilizar\" + 0.000*\"criado\"\n",
            "Topic: 278 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 279 \n",
            "Words: 0.226*\"technolog\" + 0.170*\"monitor\" + 0.170*\"devic\" + 0.084*\"electr\" + 0.081*\"nomin\" + 0.058*\"academ\" + 0.011*\"classroom\" + 0.009*\"associ\" + 0.006*\"analyz\" + 0.003*\"synchron\"\n",
            "Topic: 280 \n",
            "Words: 0.209*\"campaign\" + 0.116*\"store\" + 0.059*\"themselv\" + 0.055*\"partial\" + 0.052*\"intens\" + 0.051*\"imagin\" + 0.045*\"confirm\" + 0.035*\"mission\" + 0.032*\"planet\" + 0.026*\"orbit\"\n",
            "Topic: 281 \n",
            "Words: 0.229*\"averag\" + 0.097*\"global\" + 0.092*\"impact\" + 0.069*\"airport\" + 0.067*\"construct\" + 0.052*\"minimum\" + 0.050*\"climat\" + 0.048*\"maximum\" + 0.039*\"electron\" + 0.035*\"variat\"\n",
            "Topic: 282 \n",
            "Words: 0.310*\"state\" + 0.279*\"popul\" + 0.225*\"unit\" + 0.031*\"consult\" + 0.016*\"calendar\" + 0.014*\"geographi\" + 0.002*\"wit\" + 0.000*\"característica\" + 0.000*\"candidato\" + 0.000*\"declarado\"\n",
            "Topic: 283 \n",
            "Words: 0.189*\"educ\" + 0.164*\"student\" + 0.104*\"higher\" + 0.055*\"consumpt\" + 0.055*\"portugues\" + 0.051*\"alcohol\" + 0.051*\"cortez\" + 0.040*\"school\" + 0.038*\"mother\" + 0.021*\"secondari\"\n",
            "Topic: 284 \n",
            "Words: 0.405*\"network\" + 0.108*\"neural\" + 0.096*\"sector\" + 0.088*\"alreadi\" + 0.058*\"linear\" + 0.042*\"autom\" + 0.041*\"technic\" + 0.017*\"secur\" + 0.006*\"approx\" + 0.006*\"fetch\"\n",
            "Topic: 285 \n",
            "Words: 0.727*\"number\" + 0.125*\"whether\" + 0.049*\"accid\" + 0.016*\"promot\" + 0.009*\"leav\" + 0.008*\"experienc\" + 0.008*\"satisfact\" + 0.000*\"prematur\" + 0.000*\"eleiçõ\" + 0.000*\"deputado\"\n",
            "Topic: 286 \n",
            "Words: 0.261*\"entir\" + 0.159*\"upload\" + 0.071*\"difficult\" + 0.069*\"previous\" + 0.057*\"especi\" + 0.055*\"easier\" + 0.033*\"enabl\" + 0.032*\"abil\" + 0.026*\"battl\" + 0.026*\"purchas\"\n",
            "Topic: 287 \n",
            "Words: 0.172*\"relationship\" + 0.135*\"incom\" + 0.119*\"insight\" + 0.112*\"financi\" + 0.096*\"return\" + 0.059*\"show\" + 0.056*\"invest\" + 0.037*\"transpar\" + 0.023*\"exploratori\" + 0.013*\"specul\"\n",
            "Topic: 288 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 289 \n",
            "Words: 0.238*\"program\" + 0.236*\"survey\" + 0.157*\"respons\" + 0.092*\"demograph\" + 0.086*\"conduct\" + 0.038*\"respond\" + 0.016*\"properli\" + 0.011*\"stackoverflow\" + 0.007*\"similarli\" + 0.002*\"apolog\"\n",
            "Topic: 290 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 291 \n",
            "Words: 0.361*\"refer\" + 0.162*\"previou\" + 0.114*\"messag\" + 0.113*\"tag\" + 0.060*\"compos\" + 0.002*\"legitim\" + 0.000*\"declarado\" + 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"disponibilizar\"\n",
            "Topic: 292 \n",
            "Words: 0.305*\"custom\" + 0.083*\"contest\" + 0.070*\"subsequ\" + 0.056*\"customerid\" + 0.040*\"earlier\" + 0.037*\"tempor\" + 0.034*\"sequenti\" + 0.031*\"ignor\" + 0.028*\"keep\" + 0.025*\"movieid\"\n",
            "Topic: 293 \n",
            "Words: 0.248*\"python\" + 0.247*\"explor\" + 0.167*\"industri\" + 0.112*\"retriev\" + 0.046*\"panda\" + 0.013*\"beginn\" + 0.011*\"game\" + 0.005*\"consol\" + 0.004*\"sophist\" + 0.000*\"disponibilizar\"\n",
            "Topic: 294 \n",
            "Words: 0.171*\"reliabl\" + 0.127*\"believ\" + 0.093*\"stream\" + 0.076*\"inspect\" + 0.040*\"unless\" + 0.020*\"irrespect\" + 0.018*\"noth\" + 0.016*\"bracket\" + 0.015*\"scrapper\" + 0.002*\"countrycsv\"\n",
            "Topic: 295 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 296 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 297 \n",
            "Words: 0.244*\"function\" + 0.138*\"encod\" + 0.123*\"express\" + 0.058*\"perhap\" + 0.047*\"biolog\" + 0.038*\"yield\" + 0.032*\"transcrib\" + 0.027*\"mutat\" + 0.023*\"biomed\" + 0.021*\"reaction\"\n",
            "Topic: 298 \n",
            "Words: 0.364*\"school\" + 0.086*\"children\" + 0.064*\"major\" + 0.062*\"earn\" + 0.047*\"graduat\" + 0.027*\"bachelor\" + 0.024*\"dispar\" + 0.020*\"disagre\" + 0.019*\"religion\" + 0.018*\"danger\"\n",
            "Topic: 299 \n",
            "Words: 0.241*\"replac\" + 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"agradeço\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 300 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 301 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 302 \n",
            "Words: 0.151*\"process\" + 0.116*\"within\" + 0.089*\"better\" + 0.086*\"singl\" + 0.081*\"understand\" + 0.077*\"pattern\" + 0.070*\"connect\" + 0.045*\"wonder\" + 0.029*\"complex\" + 0.027*\"movement\"\n",
            "Topic: 303 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 304 \n",
            "Words: 0.155*\"directori\" + 0.138*\"insur\" + 0.123*\"agreement\" + 0.113*\"join\" + 0.037*\"medicar\" + 0.029*\"dental\" + 0.026*\"medicaid\" + 0.012*\"marketplac\" + 0.000*\"disgust\" + 0.000*\"engenheiro\"\n",
            "Topic: 305 \n",
            "Words: 0.174*\"build\" + 0.106*\"definit\" + 0.064*\"neighborhood\" + 0.058*\"altern\" + 0.056*\"squar\" + 0.053*\"boundari\" + 0.048*\"evid\" + 0.034*\"estat\" + 0.029*\"push\" + 0.029*\"cooper\"\n",
            "Topic: 306 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 307 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 308 \n",
            "Words: 0.347*\"element\" + 0.078*\"direct\" + 0.068*\"practic\" + 0.060*\"expenditur\" + 0.059*\"assess\" + 0.049*\"central\" + 0.036*\"assum\" + 0.025*\"focus\" + 0.024*\"total\" + 0.022*\"critic\"\n",
            "Topic: 309 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 310 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 311 \n",
            "Words: 0.229*\"attempt\" + 0.225*\"convert\" + 0.090*\"cricket\" + 0.027*\"decod\" + 0.019*\"batsman\" + 0.018*\"openend\" + 0.013*\"httpcricsheetorg\" + 0.013*\"usabl\" + 0.009*\"flag\" + 0.007*\"umpir\"\n",
            "Topic: 312 \n",
            "Words: 0.427*\"descript\" + 0.198*\"describ\" + 0.145*\"insid\" + 0.080*\"address\" + 0.028*\"specifi\" + 0.025*\"sometim\" + 0.018*\"provinc\" + 0.011*\"grow\" + 0.005*\"bottl\" + 0.003*\"accomplish\"\n",
            "Topic: 313 \n",
            "Words: 0.276*\"august\" + 0.144*\"februari\" + 0.000*\"conclusão\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"agradeço\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"criado\"\n",
            "Topic: 314 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 315 \n",
            "Words: 0.734*\"collect\" + 0.106*\"rank\" + 0.036*\"compris\" + 0.028*\"frequent\" + 0.006*\"supplementari\" + 0.002*\"bayesian\" + 0.000*\"conclusão\" + 0.000*\"candidato\" + 0.000*\"doaçõ\" + 0.000*\"disponibilizar\"\n",
            "Topic: 316 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 317 \n",
            "Words: 0.323*\"specif\" + 0.207*\"observ\" + 0.090*\"gather\" + 0.087*\"decid\" + 0.056*\"dioxid\" + 0.032*\"pollut\" + 0.029*\"arrang\" + 0.015*\"carbon\" + 0.008*\"nitrogen\" + 0.004*\"monoxid\"\n",
            "Topic: 318 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 319 \n",
            "Words: 0.269*\"possess\" + 0.117*\"togeth\" + 0.096*\"call\" + 0.063*\"probabl\" + 0.054*\"advanc\" + 0.046*\"bet\" + 0.036*\"thorough\" + 0.032*\"tournament\" + 0.021*\"champion\" + 0.016*\"obvious\"\n",
            "Topic: 320 \n",
            "Words: 0.304*\"train\" + 0.093*\"section\" + 0.081*\"architectur\" + 0.069*\"spanish\" + 0.039*\"preprocess\" + 0.038*\"arab\" + 0.033*\"improv\" + 0.028*\"french\" + 0.028*\"beauti\" + 0.027*\"parent\"\n",
            "Topic: 321 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 322 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 323 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 324 \n",
            "Words: 0.226*\"offic\" + 0.189*\"make\" + 0.093*\"happen\" + 0.056*\"suitabl\" + 0.040*\"doubl\" + 0.031*\"disclosur\" + 0.030*\"regularli\" + 0.029*\"excit\" + 0.028*\"immedi\" + 0.026*\"hardwar\"\n",
            "Topic: 325 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 326 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n",
            "Topic: 327 \n",
            "Words: 0.304*\"dataset\" + 0.134*\"context\" + 0.133*\"content\" + 0.125*\"acknowledg\" + 0.111*\"inspir\" + 0.051*\"differ\" + 0.028*\"thank\" + 0.011*\"visual\" + 0.011*\"includ\" + 0.010*\"compar\"\n",
            "Topic: 328 \n",
            "Words: 0.000*\"conclusão\" + 0.000*\"criado\" + 0.000*\"antun\" + 0.000*\"candidato\" + 0.000*\"característica\" + 0.000*\"robotsthi\" + 0.000*\"disponibilizar\" + 0.000*\"deputado\" + 0.000*\"lakadsensgmailcom\" + 0.000*\"declarado\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEQMgaeb_WSq",
        "colab_type": "code",
        "outputId": "f9c599d1-0620-47ec-9725-1a495fe80e21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "from gensim.models.coherencemodel import CoherenceModel\n",
        "\n",
        "coherence_val = CoherenceModel(model=lda_model, texts=stemmed_dataset, dictionary=dictionary_of_words, coherence='c_v').get_coherence()\n",
        "\n",
        "print('Coherence Score: ', coherence_val)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Coherence Score:  0.4049046232597778\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oeQB8OODKRho",
        "colab_type": "text"
      },
      "source": [
        "Now we will change the number of topics. Increasing it from 10 to 280 to find for which number of topic the model givers the better result."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W7sBlYGKKQyc",
        "colab_type": "code",
        "outputId": "7e30661a-8c88-4e8f-a456-fdb7c8cc112d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "source": [
        "models=[]\n",
        "coherence_value = []\n",
        "for topic_number in range(10,331,10):\n",
        "  lda_model = gensim.models.ldamodel.LdaModel(corpus=word_corpus,\n",
        "                                           id2word=dictionary_of_words,\n",
        "                                           num_topics=topic_number, \n",
        "                                           random_state=101,\n",
        "                                           update_every=1,\n",
        "                                           chunksize=100,\n",
        "                                           passes=50,\n",
        "                                           alpha='auto',\n",
        "                                           per_word_topics=True)\n",
        "  models.append(lda_model)\n",
        "  coherence_model_lda = CoherenceModel(model=lda_model, texts=stemmed_dataset, dictionary=dictionary_of_words, coherence='c_v')\n",
        "  coherence_lda = coherence_model_lda.get_coherence()\n",
        "  coherence_value.append(coherence_lda)\n",
        "  print(\"number of topics \",topic_number,\"coherence_value :\" , coherence_lda)\n",
        "\n",
        "\n",
        "  \n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of topics  10 coherence_value : 0.4653519035892977\n",
            "number of topics  20 coherence_value : 0.4550750398971183\n",
            "number of topics  30 coherence_value : 0.44863968258604214\n",
            "number of topics  40 coherence_value : 0.4547481059253995\n",
            "number of topics  50 coherence_value : 0.44301625072325157\n",
            "number of topics  60 coherence_value : 0.46445707924657637\n",
            "number of topics  70 coherence_value : 0.45624869542098273\n",
            "number of topics  80 coherence_value : 0.4203470515461817\n",
            "number of topics  90 coherence_value : 0.4351616717015385\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/gensim/models/ldamodel.py:1023: RuntimeWarning: divide by zero encountered in log\n",
            "  diff = np.log(self.expElogbeta)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "number of topics  100 coherence_value : 0.3763536733136574\n",
            "number of topics  110 coherence_value : 0.3708493032022244\n",
            "number of topics  120 coherence_value : 0.378068715541249\n",
            "number of topics  130 coherence_value : 0.3622266272804484\n",
            "number of topics  140 coherence_value : 0.37962929112422766\n",
            "number of topics  150 coherence_value : 0.3597523548671496\n",
            "number of topics  160 coherence_value : 0.3628240549325654\n",
            "number of topics  170 coherence_value : 0.3551351508307952\n",
            "number of topics  180 coherence_value : 0.36675278827360513\n",
            "number of topics  190 coherence_value : 0.35207241122889305\n",
            "number of topics  200 coherence_value : 0.3537770200770528\n",
            "number of topics  210 coherence_value : 0.361980150523584\n",
            "number of topics  220 coherence_value : 0.36081005515755327\n",
            "number of topics  230 coherence_value : 0.35287025691724966\n",
            "number of topics  240 coherence_value : 0.350588711476604\n",
            "number of topics  250 coherence_value : 0.3530185667668948\n",
            "number of topics  260 coherence_value : 0.35878173109952133\n",
            "number of topics  270 coherence_value : 0.3559889530867674\n",
            "number of topics  280 coherence_value : 0.34948493149767634\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ov0JqS2yCq2g",
        "colab_type": "text"
      },
      "source": [
        "We can see that if we increase the number of topics   the coherence value is decreasing. We select a number from above as the number of topic for which the model outputs better coherence value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "76hMukaCrpB-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lda_model1 = gensim.models.ldamodel.LdaModel(corpus=word_corpus,\n",
        "                                           id2word=dictionary_of_words,\n",
        "                                           num_topics=60, \n",
        "                                           random_state=1,\n",
        "                                           update_every=1,\n",
        "                                           chunksize=100,\n",
        "                                           passes=50,\n",
        "                                           alpha='auto',\n",
        "                                           per_word_topics=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LdyVlXAAtWiQ",
        "colab_type": "code",
        "outputId": "071956af-115c-4a7b-e70f-4b0b31cbbbf7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "\n",
        "from gensim.models.coherencemodel import CoherenceModel\n",
        "\n",
        "\n",
        "# Compute Coherence Score\n",
        "cohr_val = CoherenceModel(model=lda_model1, texts=stemmed_dataset, dictionary=dictionary_of_words, coherence='c_v').get_coherence()\n",
        "\n",
        "print('\\nCoherence Score: ', cohr_val)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Coherence Score:  0.45274439234199604\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cc7ojZFQDPXj",
        "colab_type": "text"
      },
      "source": [
        "## **Building LdaMulticore model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agCVKCA8xFFr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from gensim.test.utils import common_corpus, common_dictionary\n",
        "lda_multicore_model = gensim.models.ldamulticore.LdaMulticore(corpus=word_corpus, \n",
        "                                                              num_topics=60, \n",
        "                                                              id2word=dictionary_of_words,                                                             \n",
        "                                                              chunksize=100, \n",
        "                                                              passes=50,                                \n",
        "                                                              alpha='symmetric',\n",
        "                                                              eta=0.1,\n",
        "                                                              decay=0.5, \n",
        "                                                              offset=1.0, \n",
        "                                                              gamma_threshold=0.001,\n",
        "                                                              random_state=101,\n",
        "                                                              minimum_probability=0.01,\n",
        "                                                              minimum_phi_value=0.01,\n",
        "                                                              per_word_topics=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4-5dPbdoxQ2Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b885e905-a577-4cf3-853b-b00052ccab57"
      },
      "source": [
        "from gensim.models.coherencemodel import CoherenceModel\n",
        "\n",
        "\n",
        "# Compute Coherence Score\n",
        "cohr_lda_multicore_model1 = CoherenceModel(model=lda_multicore_model, texts=stemmed_dataset, dictionary=dictionary_of_words, coherence='c_v').get_coherence()\n",
        "\n",
        "print('\\nCoherence Score: ', cohr_lda_multicore_model1)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Coherence Score:  0.500895728531509\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBO-rimP5cUA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from gensim.test.utils import common_corpus, common_dictionary\n",
        "lda_multicore_model2 = gensim.models.ldamulticore.LdaMulticore(corpus=word_corpus, \n",
        "                                                              num_topics=329, \n",
        "                                                              id2word=dictionary_of_words,                                                             \n",
        "                                                              chunksize=100, \n",
        "                                                              passes=50,                                \n",
        "                                                              alpha='symmetric',\n",
        "                                                              eta=0.1,\n",
        "                                                              decay=0.5, \n",
        "                                                              offset=1.0, \n",
        "                                                              gamma_threshold=0.001,\n",
        "                                                              random_state=101,\n",
        "                                                              minimum_probability=0.01,\n",
        "                                                              minimum_phi_value=0.01,\n",
        "                                                              per_word_topics=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pEsjdDPR9uRS",
        "colab_type": "code",
        "outputId": "2bb9ddd1-e7ef-4b74-ef92-4f1039102e30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "from gensim.models.coherencemodel import CoherenceModel\n",
        "\n",
        "\n",
        "# Compute Coherence Score\n",
        "cohr_lda_multicore_model2 = CoherenceModel(model=lda_multicore_model2, texts=stemmed_dataset, dictionary=dictionary_of_words, coherence='c_v').get_coherence()\n",
        "\n",
        "print('\\nCoherence Score: ', cohr_lda_multicore_model2)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Coherence Score:  0.40031940029702423\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QAJJXW2Ds_db",
        "colab_type": "text"
      },
      "source": [
        "## **Evaluation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8rJedV6c2WPR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "d0f708b9-6f61-4cc0-aaf4-87699e006820"
      },
      "source": [
        "v = lda_model[word_corpus[2]]\n",
        "print(type(lda_model[word_corpus[2]]))\n",
        "z=sorted(v[0], key=lambda tup: -1*tup[1])\n",
        "print(z)\n",
        "print(v[0])"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'tuple'>\n",
            "[(27, 0.20599864), (101, 0.07712153), (33, 0.05804071), (302, 0.034482528), (69, 0.032778475), (14, 0.030817678), (49, 0.02679516), (77, 0.023056721), (30, 0.018040096), (156, 0.015705438), (15, 0.015044093), (146, 0.012555125), (53, 0.012308318), (236, 0.012272188), (235, 0.012073053), (169, 0.011946721), (55, 0.011577829), (194, 0.011440581), (253, 0.011361394)]\n",
            "[(14, 0.030817678), (15, 0.015044093), (27, 0.20599864), (30, 0.018040096), (33, 0.05804071), (49, 0.02679516), (53, 0.012308318), (55, 0.011577829), (69, 0.032778475), (77, 0.023056721), (101, 0.07712153), (146, 0.012555125), (156, 0.015705438), (169, 0.011946721), (194, 0.011440581), (235, 0.012073053), (236, 0.012272188), (253, 0.011361394), (302, 0.034482528)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmMgGir1_RNS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b408afd1-b96e-46e2-82ab-46c26bf7bb83"
      },
      "source": [
        "for  index,score in sorted(lda_model[word_corpus[2]][0], key=lambda tup: -1*tup[1]):\n",
        "    print(\"\\nScore: {}\\t \\nTopic: {}\".format(score, lda_model.print_topic(index, 10)))"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Score: 0.20599859952926636\t \n",
            "Topic: 0.112*\"registr\" + 0.101*\"revenu\" + 0.089*\"figur\" + 0.067*\"budget\" + 0.066*\"client\" + 0.042*\"check\" + 0.036*\"basic\" + 0.032*\"treat\" + 0.028*\"complic\" + 0.025*\"limit\"\n",
            "\n",
            "Score: 0.07712119817733765\t \n",
            "Topic: 0.143*\"includ\" + 0.139*\"inform\" + 0.082*\"avail\" + 0.076*\"provid\" + 0.068*\"differ\" + 0.060*\"origin\" + 0.044*\"gener\" + 0.044*\"detail\" + 0.035*\"version\" + 0.033*\"sourc\"\n",
            "\n",
            "Score: 0.058055225759744644\t \n",
            "Topic: 0.375*\"dataset\" + 0.207*\"contain\" + 0.077*\"follow\" + 0.062*\"creat\" + 0.034*\"process\" + 0.034*\"download\" + 0.027*\"perform\" + 0.022*\"variou\" + 0.017*\"multipl\" + 0.016*\"befor\"\n",
            "\n",
            "Score: 0.0344846136868\t \n",
            "Topic: 0.201*\"public\" + 0.151*\"pleas\" + 0.111*\"research\" + 0.106*\"field\" + 0.102*\"through\" + 0.085*\"across\" + 0.045*\"cover\" + 0.022*\"paper\" + 0.019*\"rather\" + 0.018*\"prepar\"\n",
            "\n",
            "Score: 0.032669298350811005\t \n",
            "Topic: 0.184*\"content\" + 0.179*\"acknowledg\" + 0.175*\"context\" + 0.174*\"dataset\" + 0.155*\"inspir\" + 0.045*\"sourc\" + 0.019*\"websit\" + 0.012*\"analyz\" + 0.009*\"upload\" + 0.008*\"anoth\"\n",
            "\n",
            "Score: 0.030817678198218346\t \n",
            "Topic: 0.217*\"author\" + 0.150*\"kernel\" + 0.088*\"publicli\" + 0.065*\"success\" + 0.057*\"studi\" + 0.046*\"queri\" + 0.034*\"stori\" + 0.033*\"bias\" + 0.032*\"bigqueri\" + 0.030*\"focus\"\n",
            "\n",
            "Score: 0.026794465258717537\t \n",
            "Topic: 0.132*\"exampl\" + 0.081*\"repres\" + 0.070*\"singl\" + 0.059*\"evalu\" + 0.058*\"correspond\" + 0.050*\"effect\" + 0.040*\"associ\" + 0.036*\"result\" + 0.032*\"algorithm\" + 0.030*\"particular\"\n",
            "\n",
            "Score: 0.023056721314787865\t \n",
            "Topic: 0.206*\"amount\" + 0.152*\"statu\" + 0.141*\"credit\" + 0.095*\"california\" + 0.067*\"dollar\" + 0.062*\"statement\" + 0.058*\"payment\" + 0.029*\"default\" + 0.018*\"afford\" + 0.014*\"marit\"\n",
            "\n",
            "Score: 0.01789219304919243\t \n",
            "Topic: 0.179*\"estim\" + 0.162*\"whether\" + 0.102*\"should\" + 0.092*\"condit\" + 0.085*\"actual\" + 0.067*\"control\" + 0.053*\"probabl\" + 0.032*\"relev\" + 0.029*\"import\" + 0.028*\"determin\"\n",
            "\n",
            "Score: 0.015704063698649406\t \n",
            "Topic: 0.139*\"introduct\" + 0.127*\"athlet\" + 0.107*\"actor\" + 0.096*\"director\" + 0.032*\"olymp\" + 0.027*\"silver\" + 0.018*\"bronz\" + 0.016*\"runtim\" + 0.015*\"popular\" + 0.015*\"montgomeri\"\n",
            "\n",
            "Score: 0.015044093132019043\t \n",
            "Topic: 0.468*\"movi\" + 0.073*\"movielen\" + 0.058*\"grouplen\" + 0.051*\"endors\" + 0.016*\"cinema\" + 0.000*\"deputado\" + 0.000*\"disponibilizar\" + 0.000*\"declarado\" + 0.000*\"engenheiro\" + 0.000*\"eleiçõ\"\n",
            "\n",
            "Score: 0.012555125169456005\t \n",
            "Topic: 0.577*\"collect\" + 0.242*\"consist\" + 0.024*\"display\" + 0.022*\"session\" + 0.018*\"exercis\" + 0.015*\"uniqu\" + 0.012*\"chosen\" + 0.008*\"leverag\" + 0.005*\"cheaper\" + 0.004*\"present\"\n",
            "\n",
            "Score: 0.012308318167924881\t \n",
            "Topic: 0.320*\"question\" + 0.310*\"commun\" + 0.194*\"answer\" + 0.080*\"attribut\" + 0.015*\"affili\" + 0.009*\"overflow\" + 0.006*\"requir\" + 0.006*\"ccbysa\" + 0.005*\"besid\" + 0.002*\"parentid\"\n",
            "\n",
            "Score: 0.01215675100684166\t \n",
            "Topic: 0.185*\"rate\" + 0.150*\"applic\" + 0.101*\"devic\" + 0.054*\"captur\" + 0.042*\"camera\" + 0.032*\"mainli\" + 0.030*\"emot\" + 0.028*\"configur\" + 0.019*\"complet\" + 0.017*\"determin\"\n",
            "\n",
            "Score: 0.012083444744348526\t \n",
            "Topic: 0.114*\"experi\" + 0.107*\"recent\" + 0.074*\"algorithm\" + 0.061*\"scientist\" + 0.056*\"valid\" + 0.053*\"determin\" + 0.030*\"differ\" + 0.027*\"requir\" + 0.024*\"techniqu\" + 0.023*\"someth\"\n",
            "\n",
            "Score: 0.012073053047060966\t \n",
            "Topic: 0.579*\"column\" + 0.119*\"subject\" + 0.116*\"second\" + 0.042*\"signal\" + 0.018*\"actual\" + 0.011*\"predefin\" + 0.011*\"correspond\" + 0.010*\"measur\" + 0.009*\"mechan\" + 0.007*\"quantum\"\n",
            "\n",
            "Score: 0.011577828787267208\t \n",
            "Topic: 0.128*\"commerci\" + 0.081*\"right\" + 0.063*\"necessari\" + 0.057*\"handl\" + 0.053*\"registri\" + 0.051*\"contract\" + 0.049*\"ownership\" + 0.046*\"infrastructur\" + 0.026*\"power\" + 0.024*\"senior\"\n",
            "\n",
            "Score: 0.0114405807107687\t \n",
            "Topic: 0.285*\"miss\" + 0.240*\"instanc\" + 0.158*\"restaur\" + 0.052*\"readm\" + 0.026*\"geospati\" + 0.010*\"depart\" + 0.010*\"approach\" + 0.008*\"maritalstatu\" + 0.008*\"placeid\" + 0.008*\"rafael\"\n",
            "\n",
            "Score: 0.011361392214894295\t \n",
            "Topic: 0.151*\"chapter\" + 0.123*\"russian\" + 0.096*\"japanes\" + 0.084*\"extend\" + 0.083*\"util\" + 0.055*\"homepag\" + 0.054*\"fellow\" + 0.005*\"undoubtedli\" + 0.000*\"declarado\" + 0.000*\"criado\"\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hc95KLZd7bHz",
        "colab_type": "text"
      },
      "source": [
        "the topic at the top got the highest probability and it is related to something like economic"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PJTOIjsnDpHZ",
        "colab_type": "text"
      },
      "source": [
        "## **Visualization**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G9o8--rW5RSC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "381a6636-bf93-4716-c16e-67e166f3020a"
      },
      "source": [
        "\n",
        "topics = lda_model.show_topics(formatted=False)\n",
        "topic_words = dict(topics[0][1])\n",
        "wordcloud.generate_from_frequencies(topic_words, max_font_size=100)\n",
        "plt.figure(figsize = (5, 5), facecolor = None) \n",
        "plt.title(\"topic 0\")\n",
        "plt.imshow(wordcloud) \n",
        "plt.axis(\"off\") \n",
        "plt.tight_layout(pad = 0) \n",
        "  \n",
        "plt.show() \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD/CAYAAADllv3BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+P8g/zLgLan5JgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BNvabHx7_4Iw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "e9658a80-d480-460f-d26d-91189e2d59b8"
      },
      "source": [
        "\n",
        "fig = plt.figure(figsize=(15,15),frameon=0)\n",
        "a = fig.add_subplot(1, 2, 1)\n",
        "topic_words = dict(topics[1][1])\n",
        "wordcloud.generate_from_frequencies(topic_words, max_font_size=100)\n",
        "imgplot = plt.imshow(wordcloud)\n",
        "a.set_title('topic 0')\n",
        "\n",
        "a = fig.add_subplot(1, 2, 2)\n",
        "topic_words = dict(topics[2][1])\n",
        "wordcloud.generate_from_frequencies(topic_words, max_font_size=100)\n",
        "imgplot = plt.imshow(wordcloud)\n",
        "\n",
        "a.set_title('topic 1')\n"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'topic 1')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA20AAAEdCAYAAACfT48iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlNGmUCgUCoVCoVAoFHHM/wdZyJh4Wr/RjQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}