[Project_107]_Topic_Modelling_LDA.ipynb

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  "metadata": {
    "colab": {
      "name": "[Project 107] Topic Modelling - LDA.ipynb",
      "provenance": [],
      "collapsed_sections": [
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      "toc_visible": true,
      "authorship_tag": "ABX9TyOnMcV4/t/untxYufV7T5pR",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/id-shiv/knowledge_base/blob/master/%5BProject_107%5D_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": "VlzB4JWE7QBc"
      },
      "source": [
        "# Topic Modelling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FTzND1XFEJw"
      },
      "source": [
        "Reference: https://medium.com/@yanlinc/how-to-build-a-lda-topic-model-using-from-text-601cdcbfd3a6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W4hSQGui9O7A"
      },
      "source": [
        "# Import"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bubI_W9W5gTv"
      },
      "source": [
        "# Run in terminal or command prompt\n",
        "# ! python3 -m spacy download en\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re, nltk, spacy, gensim\n",
        "# Sklearn\n",
        "from sklearn.decomposition import LatentDirichletAllocation, TruncatedSVD\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from pprint import pprint\n",
        "# Plotting tools\n",
        "import pyLDAvis\n",
        "import pyLDAvis.sklearn\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 225,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u2xxl53r4SBQ"
      },
      "source": [
        "# Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GhBoElRg9yms"
      },
      "source": [
        "texts = \"\"\"The goal of this testing is to satisfy users and it mainly concentrates on the following parameters of a system:\n",
        "\n",
        "The effectiveness of the system\n",
        "\n",
        "Is the system is easy to learn?\n",
        "Is the system useful and adds value to the target audience?\n",
        "Are Content, Color, Icons, Images used are aesthetically pleasing?\n",
        "Efficiency\n",
        "\n",
        "Little navigation should be required to reach the desired screen or webpage, and scrollbars should be used infrequently.\n",
        "Uniformity in the format of screen/pages in your application/website.\n",
        "Option to search within your software application or website.\n",
        "Accuracy\n",
        "\n",
        "No outdated or incorrect data like contact information/address should be present.\n",
        "No broken links should be present.\n",
        "User Friendliness\n",
        "\n",
        "Controls used should be self-explanatory and must not require training to operate\n",
        "Help should be provided for the users to understand the application/website\n",
        "Alignment with the above goals helps in effective usability testing\n",
        "How to do Usability Testing: Complete Process\n",
        "Usability testing process consists of the following phases\n",
        "\n",
        "Usability Testing Tutorial: Need, Process, Best Practice\n",
        "\n",
        "Planning:-  During this phase the goals of usability test are determined. Having volunteers sit in front of your application and recording their actions is not a goal. You need to determine critical functionalities and objectives of the system. You need to assign tasks to your testers, which exercise these critical functionalities. During this phase, the usability testing method, number & demographics of usability testers, test report formats are also determined\n",
        "\n",
        "Recruiting: During this phase, you recruit the desired number of testers as per your usability test plan. Finding testers who match your demographic (age, sex etc.) and professional ( education, job etc.) profile can take time.\n",
        "\n",
        "Usability Testing: During this phase, usability tests are actually executed.\n",
        "\n",
        "Data Analysis: Data from usability tests is thoroughly analyzed to derive meaningful inferences and give actionable recommendations to improve the overall usability of your product.\n",
        "\n",
        "Reporting: Findings of the usability test is shared with all concerned stakeholders which can include designer, developer, client, and CEO\n",
        "\n",
        "Methods of Usability Testing: 2 Techniques\n",
        "There are two methods available to do usability testing -\n",
        "\n",
        "Laboratory Usability Testing\n",
        "Remote Usability Testing\n",
        "Laboratory Usability Testing:. This testing is conducted in a separate lab room in presence of the observers. The testers are assigned tasks to execute. The role of the observer is to monitor the behavior of the testers and report the outcome of testing. The observer remains silent during the course of testing.  In this testing, both observers and testers are present in a same physical location.\n",
        "\n",
        "Remote Usability Testing: Under this testing observers and testers are remotely located. Testers access the System Under Test, remotely and perform assigned tasks. Tester's voice , screen activity , testers facial expressions are recorded by an automated software. Observers analyze this data and report findings of the test. Example of such a software - http://silverbackapp.com/\n",
        "\n",
        "\n",
        "How many users do you need ?\n",
        "Usability Testing Tutorial: Need, Process, Best Practice\n",
        "\n",
        "Research (Virzi, 1992 and Neilsen Landauer, 1993) indicates that 5 users are enough to uncover 80% of usability problems. Some researchers suggest other numbers.\n",
        "\n",
        "The truth is , the actual number of the user required depends on the complexity of the given application and your usability goals. Increase in usability participants results into increased cost , planning , participant management and data analysis.\n",
        "\n",
        "But as a general guideline, if you on a small budget and interested in DIY usability testing 5 is a good number to start with. If budget is not a constraint its best consult experienced professionals to determine the number of users.\n",
        "\n",
        "UX Testing Checklist\n",
        "The primary goal of this testing is to find crucial usability problems before the product is launched. Following things have to be considered to make a testing success:\n",
        "\n",
        "Start the UX testing during the early stage of design and development\n",
        "It's a good practice to conduct usability testing on your competitor's product before you begin development. This will help you determine usability standards for your target audience \n",
        "Select the appropriate users to test the system(Can be experts/non-experts users/50-50 of Experts and Non-Experts users)\n",
        "Use a bandwidth shaper . For instance , your target audience has poor network connectivity , limit network bandwidth to say 56 Kbps for your usability testers. \n",
        "Testers need to concentrate on  critical  & frequently used functionalities of the system.\n",
        "Assign a single observer to each tester. This helps observer to accurately note tester's behavior. If an observer is assigned to multiple testers, results may be compromised\n",
        "Educate Designers and Developers that this testing outcomes is not a sign of failure but it's a sign of Improvement\n",
        "Usability Testing Advantages\n",
        "As with anything in life, usability testing has its merits and de-merits. Let's look at them\n",
        "\n",
        "It helps uncover usability issues before the product is marketed.\n",
        "It helps improve end-user satisfaction\n",
        "It makes your system highly effective and efficient\n",
        "It helps gather true feedback from your target audience who actually use your system during a usability test. You do not need to rely on \"opinions\" from random people.\n",
        "Usability Testing Disadvantages\n",
        "Cost is a major consideration in usability testing. It takes lots of resources to set up a Usability Test Lab. Recruiting and management of usability testers can also be expensive\n",
        "However, these costs pay themselves up in form of higher customer satisfaction, retention and repeat business. Usability testing is therefore highly recommended.\n",
        "\n",
        "\"\"\""
      ],
      "execution_count": 226,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bh9viOTY8T_7"
      },
      "source": [
        "# Clean data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LcbzbLp_0yw",
        "outputId": "94bcfc4d-ef92-4ab7-90c9-eb1d69158233"
      },
      "source": [
        "documents = texts.split('\\n')\n",
        "documents = [document.strip() for document in documents if len(document) > 1]\n",
        "documents\n",
        "len(documents)"
      ],
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "53"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y52_VNK_6Xbb"
      },
      "source": [
        "# EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "3v8Lq79W6YWO",
        "outputId": "ef600762-1fb5-4814-9ad6-06a4e97df472"
      },
      "source": [
        "# input : documents (list of document texts)\n",
        "\n",
        "# Import the wordcloud library\n",
        "from wordcloud import WordCloud\n",
        "# Join the different processed titles together.\n",
        "long_string = '. '.join(documents)\n",
        "# Create a WordCloud object\n",
        "wordcloud = WordCloud(background_color=\"white\", max_words=5000, contour_width=3, contour_color='steelblue')\n",
        "# Generate a word cloud\n",
        "wordcloud.generate(long_string)\n",
        "# Visualize the word cloud\n",
        "wordcloud.to_image()"
      ],
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=400x200 at 0x7F67BA223320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l7x_RVFm6n5Q"
      },
      "source": [
        "# Prepare Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gc7TRHzqG05R"
      },
      "source": [
        "## Words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsl337PD-rI0",
        "outputId": "08c23608-a0b7-4ad5-8329-375cd87986e2"
      },
      "source": [
        "# break documents into words\n",
        "def sent_to_words(sentences):\n",
        "    for sentence in sentences:\n",
        "        yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))  # deacc=True removes punctuations\n",
        "\n",
        "words = list(sent_to_words(documents))\n",
        "\n",
        "words[:5]"
      ],
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['the',\n",
              "  'goal',\n",
              "  'of',\n",
              "  'this',\n",
              "  'testing',\n",
              "  'is',\n",
              "  'to',\n",
              "  'satisfy',\n",
              "  'users',\n",
              "  'and',\n",
              "  'it',\n",
              "  'mainly',\n",
              "  'concentrates',\n",
              "  'on',\n",
              "  'the',\n",
              "  'following',\n",
              "  'parameters',\n",
              "  'of',\n",
              "  'system'],\n",
              " ['the', 'effectiveness', 'of', 'the', 'system'],\n",
              " ['is', 'the', 'system', 'is', 'easy', 'to', 'learn'],\n",
              " ['is',\n",
              "  'the',\n",
              "  'system',\n",
              "  'useful',\n",
              "  'and',\n",
              "  'adds',\n",
              "  'value',\n",
              "  'to',\n",
              "  'the',\n",
              "  'target',\n",
              "  'audience'],\n",
              " ['are',\n",
              "  'content',\n",
              "  'color',\n",
              "  'icons',\n",
              "  'images',\n",
              "  'used',\n",
              "  'are',\n",
              "  'aesthetically',\n",
              "  'pleasing']]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzC-OIJ7G_6B"
      },
      "source": [
        "## Lemmatize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A45F6y6t-7Mj",
        "outputId": "23f6f425-1f05-4bf2-cefd-feb5ffd112c4"
      },
      "source": [
        "# lemmatization\n",
        "def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']): #'NOUN', 'ADJ', 'VERB', 'ADV'\n",
        "    texts_out = []\n",
        "    for sent in texts:\n",
        "        doc = nlp(\" \".join(sent)) \n",
        "        texts_out.append(\" \".join([token.lemma_ if token.lemma_ not in ['-PRON-'] else '' for token in doc if token.pos_ in allowed_postags]))\n",
        "    return texts_out\n",
        "\n",
        "# Initialize spacy ‘en’ model, keeping only tagger component (for efficiency)\n",
        "# Run in terminal: python -m spacy download en\n",
        "nlp = spacy.load('en', disable=['parser', 'ner'])\n",
        "# Do lemmatization keeping only Noun, Adj, Verb, Adverb\n",
        "data_lemmatized = lemmatization(words, allowed_postags=['NOUN', 'VERB']) #select noun and verb\n",
        "\n",
        "data_lemmatized[:10]"
      ],
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['goal testing satisfy user concentrate follow parameter system',\n",
              " 'effectiveness system',\n",
              " 'system learn',\n",
              " 'system add value target audience',\n",
              " 'content color icon image use',\n",
              " 'efficiency',\n",
              " 'navigation should require reach desire screen webpage scrollbar should use',\n",
              " 'uniformity format screen page application website',\n",
              " 'option search software application website',\n",
              " 'accuracy']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SgQCrNSQHCUG"
      },
      "source": [
        "## Vectorize: Document-Word Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0_c84U3W_qdR",
        "outputId": "9f0855a3-d236-4f64-c838-da355da48980"
      },
      "source": [
        "# create document-word matrix\n",
        "vectorizer = CountVectorizer(analyzer='word',       \n",
        "                             min_df=2,\n",
        "# minimum reqd occurences of a word \n",
        "                             stop_words='english',             \n",
        "# remove stop words\n",
        "                             lowercase=True,                   \n",
        "# convert all words to lowercase\n",
        "                            #  token_pattern='[a-zA-Z0-9]{3,}',  \n",
        "# num chars > 3\n",
        "                             # max_features=50000,             \n",
        "# max number of uniq words    \n",
        ")\n",
        "data_vectorized = vectorizer.fit_transform(data_lemmatized)\n",
        "\n",
        "data_vectorized"
      ],
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<53x55 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 226 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hrjAXu1zAYBT"
      },
      "source": [
        "# Build Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CPTj7otQAZbf",
        "outputId": "72bef7c2-fee5-48f8-a864-71800e8dc6aa"
      },
      "source": [
        "# Build LDA Model\n",
        "lda_model = LatentDirichletAllocation(n_components=10,               # Number of topics\n",
        "                                      max_iter=10,               \n",
        "# Max learning iterations\n",
        "                                      learning_method='online',   \n",
        "                                      random_state=100,          \n",
        "# Random state\n",
        "                                      batch_size=32,            \n",
        "# n docs in each learning iter\n",
        "                                      evaluate_every = -1,       \n",
        "# compute perplexity every n iters, default: Don't\n",
        "                                      n_jobs = -1,               \n",
        "# Use all available CPUs\n",
        "                                     )\n",
        "lda_output = lda_model.fit_transform(data_vectorized)\n",
        "print(lda_model)  # Model attributes"
      ],
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LatentDirichletAllocation(batch_size=32, doc_topic_prior=None,\n",
            "                          evaluate_every=-1, learning_decay=0.7,\n",
            "                          learning_method='online', learning_offset=10.0,\n",
            "                          max_doc_update_iter=100, max_iter=10,\n",
            "                          mean_change_tol=0.001, n_components=10, n_jobs=-1,\n",
            "                          perp_tol=0.1, random_state=100, topic_word_prior=None,\n",
            "                          total_samples=1000000.0, verbose=0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XzXeSitf8_NU"
      },
      "source": [
        "# Analyze"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WFWQGdDv77fK"
      },
      "source": [
        "# ! pip install pyldavis"
      ],
      "execution_count": 233,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIJcxu7hBptF",
        "outputId": "e069e671-61c7-406a-d9cb-36d521d7158b"
      },
      "source": [
        "# Log Likelyhood: Higher the better\n",
        "print(\"Log Likelihood: \", lda_model.score(data_vectorized))\n",
        "# Perplexity: Lower the better. Perplexity = exp(-1. * log-likelihood per word)\n",
        "print(\"Perplexity: \", lda_model.perplexity(data_vectorized))\n",
        "# See model parameters\n",
        "pprint(lda_model.get_params())"
      ],
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Log Likelihood:  -1231.1832298452664\n",
            "Perplexity:  102.36126636331025\n",
            "{'batch_size': 32,\n",
            " 'doc_topic_prior': None,\n",
            " 'evaluate_every': -1,\n",
            " 'learning_decay': 0.7,\n",
            " 'learning_method': 'online',\n",
            " 'learning_offset': 10.0,\n",
            " 'max_doc_update_iter': 100,\n",
            " 'max_iter': 10,\n",
            " 'mean_change_tol': 0.001,\n",
            " 'n_components': 10,\n",
            " 'n_jobs': -1,\n",
            " 'perp_tol': 0.1,\n",
            " 'random_state': 100,\n",
            " 'topic_word_prior': None,\n",
            " 'total_samples': 1000000.0,\n",
            " 'verbose': 0}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aBfX4KuqBxY-"
      },
      "source": [
        "## Grid search"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VQblqVodBzd3"
      },
      "source": [
        "Determine the best LDA model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsxcRgI3B3Ux",
        "outputId": "d01416de-b22b-4b7e-d96b-0b8ecc0e4fdf"
      },
      "source": [
        "# Define Search Param\n",
        "search_params = {'n_components': [10, 15, 20, 25, 30], 'learning_decay': [.5, .7, .9]}\n",
        "# Init the Model\n",
        "lda = LatentDirichletAllocation(max_iter=5, learning_method='online', learning_offset=50.,random_state=0)\n",
        "# Init Grid Search Class\n",
        "model = GridSearchCV(lda, param_grid=search_params)\n",
        "# Do the Grid Search\n",
        "model.fit(data_vectorized)\n",
        "GridSearchCV(cv=None, error_score='raise',\n",
        "       estimator=LatentDirichletAllocation(batch_size=128, doc_topic_prior=None,\n",
        "             evaluate_every=-1, learning_decay=0.7, learning_method=None,\n",
        "             learning_offset=10.0, max_doc_update_iter=100, max_iter=10,\n",
        "             mean_change_tol=0.001, n_components=10, n_jobs=1,\n",
        "             perp_tol=0.1, random_state=None,\n",
        "             topic_word_prior=None, total_samples=1000000.0, verbose=0),\n",
        "       iid=True, n_jobs=1,\n",
        "       param_grid={'n_topics': [10, 15, 20, 25, 30], 'learning_decay': [0.5, 0.7, 0.9]},\n",
        "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
        "       scoring=None, verbose=0)"
      ],
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=None, error_score='raise',\n",
              "             estimator=LatentDirichletAllocation(batch_size=128,\n",
              "                                                 doc_topic_prior=None,\n",
              "                                                 evaluate_every=-1,\n",
              "                                                 learning_decay=0.7,\n",
              "                                                 learning_method=None,\n",
              "                                                 learning_offset=10.0,\n",
              "                                                 max_doc_update_iter=100,\n",
              "                                                 max_iter=10,\n",
              "                                                 mean_change_tol=0.001,\n",
              "                                                 n_components=10, n_jobs=1,\n",
              "                                                 perp_tol=0.1,\n",
              "                                                 random_state=None,\n",
              "                                                 topic_word_prior=None,\n",
              "                                                 total_samples=1000000.0,\n",
              "                                                 verbose=0),\n",
              "             iid=True, n_jobs=1,\n",
              "             param_grid={'learning_decay': [0.5, 0.7, 0.9],\n",
              "                         'n_topics': [10, 15, 20, 25, 30]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 235
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZDemAO8PCT2r",
        "outputId": "724bfc02-3e1e-4a23-9256-00c5765c9c8d"
      },
      "source": [
        "# Best Model\n",
        "best_lda_model = model.best_estimator_\n",
        "print(\"Best Model: \", model.best_estimator_)\n",
        "# Model Parameters\n",
        "print(\"Best Model's Params: \", model.best_params_)\n",
        "# Log Likelihood Score\n",
        "print(\"Best Log Likelihood Score: \", model.best_score_)\n",
        "# Perplexity\n",
        "print(\"Model Perplexity: \", best_lda_model.perplexity(data_vectorized))"
      ],
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Best Model:  LatentDirichletAllocation(batch_size=128, doc_topic_prior=None,\n",
            "                          evaluate_every=-1, learning_decay=0.5,\n",
            "                          learning_method='online', learning_offset=50.0,\n",
            "                          max_doc_update_iter=100, max_iter=5,\n",
            "                          mean_change_tol=0.001, n_components=10, n_jobs=None,\n",
            "                          perp_tol=0.1, random_state=0, topic_word_prior=None,\n",
            "                          total_samples=1000000.0, verbose=0)\n",
            "Best Model's Params:  {'learning_decay': 0.5, 'n_components': 10}\n",
            "Best Log Likelihood Score:  -693.2830144663906\n",
            "Model Perplexity:  327.36609114273995\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W8ozXxKCCcG0"
      },
      "source": [
        "## Dominant Topic"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "b_ZpQFI6Cetz",
        "outputId": "c6044f23-4a08-4bc1-89dc-26ab51def112"
      },
      "source": [
        "# Create Document — Topic Matrix\n",
        "lda_output = best_lda_model.transform(data_vectorized)\n",
        "# column names\n",
        "topicnames = [\"Topic\" + str(i) for i in range(best_lda_model.n_components)]\n",
        "# index names\n",
        "docnames = [\"Doc\" + str(i) for i in range(len(data))]\n",
        "# Make the pandas dataframe\n",
        "df_document_topic = pd.DataFrame(np.round(lda_output, 2), columns=topicnames, index=docnames)\n",
        "# Get dominant topic for each document\n",
        "dominant_topic = np.argmax(df_document_topic.values, axis=1)\n",
        "df_document_topic['dominant_topic'] = dominant_topic\n",
        "# Styling\n",
        "def color_green(val):\n",
        " color = 'green' if val > .1 else 'black'\n",
        " return 'color: {col}'.format(col=color)\n",
        "def make_bold(val):\n",
        " weight = 700 if val > .1 else 400\n",
        " return 'font-weight: {weight}'.format(weight=weight)\n",
        "# Apply Style\n",
        "df_document_topics = df_document_topic.head(15).style.applymap(color_green).applymap(make_bold)\n",
        "df_document_topics"
      ],
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<style  type=\"text/css\" >\n",
              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              "            color:  black;\n",
              "            font-weight:  400;\n",
              "        }#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row0_col2,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row0_col10,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row3_col6,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row3_col10,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row4_col1,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row4_col10,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row6_col1,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row6_col10,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row7_col3,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row7_col10,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row8_col3,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row8_col10,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row10_col8,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row10_col10,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row13_col1,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row13_col10,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row14_col3,#T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row14_col10{\n",
              "            color:  green;\n",
              "            font-weight:  700;\n",
              "        }</style><table id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Topic0</th>        <th class=\"col_heading level0 col1\" >Topic1</th>        <th class=\"col_heading level0 col2\" >Topic2</th>        <th class=\"col_heading level0 col3\" >Topic3</th>        <th class=\"col_heading level0 col4\" >Topic4</th>        <th class=\"col_heading level0 col5\" >Topic5</th>        <th class=\"col_heading level0 col6\" >Topic6</th>        <th class=\"col_heading level0 col7\" >Topic7</th>        <th class=\"col_heading level0 col8\" >Topic8</th>        <th class=\"col_heading level0 col9\" >Topic9</th>        <th class=\"col_heading level0 col10\" >dominant_topic</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >Doc0</th>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.850000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row0_col8\" class=\"data row0 col8\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row0_col9\" class=\"data row0 col9\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row0_col10\" class=\"data row0 col10\" >2</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >Doc1</th>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row1_col8\" class=\"data row1 col8\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row1_col9\" class=\"data row1 col9\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row1_col10\" class=\"data row1 col10\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >Doc2</th>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row2_col8\" class=\"data row2 col8\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row2_col9\" class=\"data row2 col9\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row2_col10\" class=\"data row2 col10\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >Doc3</th>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >0.700000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row3_col8\" class=\"data row3 col8\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row3_col9\" class=\"data row3 col9\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row3_col10\" class=\"data row3 col10\" >6</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >Doc4</th>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.550000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row4_col8\" class=\"data row4 col8\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row4_col9\" class=\"data row4 col9\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row4_col10\" class=\"data row4 col10\" >1</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >Doc5</th>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row5_col8\" class=\"data row5 col8\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row5_col9\" class=\"data row5 col9\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row5_col10\" class=\"data row5 col10\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >Doc6</th>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.820000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row6_col8\" class=\"data row6 col8\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row6_col9\" class=\"data row6 col9\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row6_col10\" class=\"data row6 col10\" >1</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >Doc7</th>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.820000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row7_col8\" class=\"data row7 col8\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row7_col9\" class=\"data row7 col9\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row7_col10\" class=\"data row7 col10\" >3</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >Doc8</th>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row8_col0\" class=\"data row8 col0\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row8_col1\" class=\"data row8 col1\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row8_col2\" class=\"data row8 col2\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row8_col3\" class=\"data row8 col3\" >0.770000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row8_col4\" class=\"data row8 col4\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row8_col5\" class=\"data row8 col5\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row8_col6\" class=\"data row8 col6\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row8_col7\" class=\"data row8 col7\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row8_col8\" class=\"data row8 col8\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row8_col9\" class=\"data row8 col9\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row8_col10\" class=\"data row8 col10\" >3</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >Doc9</th>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row9_col0\" class=\"data row9 col0\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row9_col1\" class=\"data row9 col1\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row9_col2\" class=\"data row9 col2\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row9_col3\" class=\"data row9 col3\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row9_col4\" class=\"data row9 col4\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row9_col5\" class=\"data row9 col5\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row9_col6\" class=\"data row9 col6\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row9_col7\" class=\"data row9 col7\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row9_col8\" class=\"data row9 col8\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row9_col9\" class=\"data row9 col9\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row9_col10\" class=\"data row9 col10\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >Doc10</th>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row10_col0\" class=\"data row10 col0\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row10_col1\" class=\"data row10 col1\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row10_col2\" class=\"data row10 col2\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row10_col3\" class=\"data row10 col3\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row10_col4\" class=\"data row10 col4\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row10_col5\" class=\"data row10 col5\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row10_col6\" class=\"data row10 col6\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row10_col7\" class=\"data row10 col7\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row10_col8\" class=\"data row10 col8\" >0.550000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row10_col9\" class=\"data row10 col9\" >0.050000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row10_col10\" class=\"data row10 col10\" >8</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >Doc11</th>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row11_col0\" class=\"data row11 col0\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row11_col1\" class=\"data row11 col1\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row11_col2\" class=\"data row11 col2\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row11_col3\" class=\"data row11 col3\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row11_col4\" class=\"data row11 col4\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row11_col5\" class=\"data row11 col5\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row11_col6\" class=\"data row11 col6\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row11_col7\" class=\"data row11 col7\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row11_col8\" class=\"data row11 col8\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row11_col9\" class=\"data row11 col9\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row11_col10\" class=\"data row11 col10\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002level0_row12\" class=\"row_heading level0 row12\" >Doc12</th>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row12_col0\" class=\"data row12 col0\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row12_col1\" class=\"data row12 col1\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row12_col2\" class=\"data row12 col2\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row12_col3\" class=\"data row12 col3\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row12_col4\" class=\"data row12 col4\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row12_col5\" class=\"data row12 col5\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row12_col6\" class=\"data row12 col6\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row12_col7\" class=\"data row12 col7\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row12_col8\" class=\"data row12 col8\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row12_col9\" class=\"data row12 col9\" >0.100000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row12_col10\" class=\"data row12 col10\" >0</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002level0_row13\" class=\"row_heading level0 row13\" >Doc13</th>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row13_col0\" class=\"data row13 col0\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row13_col1\" class=\"data row13 col1\" >0.700000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row13_col2\" class=\"data row13 col2\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row13_col3\" class=\"data row13 col3\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row13_col4\" class=\"data row13 col4\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row13_col5\" class=\"data row13 col5\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row13_col6\" class=\"data row13 col6\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row13_col7\" class=\"data row13 col7\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row13_col8\" class=\"data row13 col8\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row13_col9\" class=\"data row13 col9\" >0.030000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row13_col10\" class=\"data row13 col10\" >1</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002level0_row14\" class=\"row_heading level0 row14\" >Doc14</th>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row14_col0\" class=\"data row14 col0\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row14_col1\" class=\"data row14 col1\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row14_col2\" class=\"data row14 col2\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row14_col3\" class=\"data row14 col3\" >0.820000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row14_col4\" class=\"data row14 col4\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row14_col5\" class=\"data row14 col5\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row14_col6\" class=\"data row14 col6\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row14_col7\" class=\"data row14 col7\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row14_col8\" class=\"data row14 col8\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row14_col9\" class=\"data row14 col9\" >0.020000</td>\n",
              "                        <td id=\"T_ab14fcd6_57f5_11eb_9c8a_0242ac1c0002row14_col10\" class=\"data row14 col10\" >3</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f67b9ea07f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ioVBnFWfCy_-",
        "outputId": "22a41004-1824-40cf-ee8a-60c6454252fb"
      },
      "source": [
        "# Topic-Keyword Matrix\n",
        "df_topic_keywords = pd.DataFrame(best_lda_model.components_)\n",
        "# Assign Column and Index\n",
        "df_topic_keywords.columns = vectorizer.get_feature_names()\n",
        "df_topic_keywords.index = topicnames\n",
        "# View\n",
        "df_topic_keywords"
      ],
      "execution_count": 238,
      "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>analyze</th>\n",
              "      <th>application</th>\n",
              "      <th>assign</th>\n",
              "      <th>audience</th>\n",
              "      <th>behavior</th>\n",
              "      <th>concentrate</th>\n",
              "      <th>conduct</th>\n",
              "      <th>cost</th>\n",
              "      <th>datum</th>\n",
              "      <th>designer</th>\n",
              "      <th>desire</th>\n",
              "      <th>determine</th>\n",
              "      <th>developer</th>\n",
              "      <th>development</th>\n",
              "      <th>execute</th>\n",
              "      <th>finding</th>\n",
              "      <th>follow</th>\n",
              "      <th>format</th>\n",
              "      <th>functionality</th>\n",
              "      <th>goal</th>\n",
              "      <th>help</th>\n",
              "      <th>improve</th>\n",
              "      <th>lab</th>\n",
              "      <th>laboratory</th>\n",
              "      <th>make</th>\n",
              "      <th>management</th>\n",
              "      <th>method</th>\n",
              "      <th>need</th>\n",
              "      <th>number</th>\n",
              "      <th>observer</th>\n",
              "      <th>outcome</th>\n",
              "      <th>phase</th>\n",
              "      <th>plan</th>\n",
              "      <th>practice</th>\n",
              "      <th>problem</th>\n",
              "      <th>process</th>\n",
              "      <th>product</th>\n",
              "      <th>record</th>\n",
              "      <th>report</th>\n",
              "      <th>require</th>\n",
              "      <th>result</th>\n",
              "      <th>satisfaction</th>\n",
              "      <th>screen</th>\n",
              "      <th>software</th>\n",
              "      <th>start</th>\n",
              "      <th>target</th>\n",
              "      <th>task</th>\n",
              "      <th>test</th>\n",
              "      <th>tester</th>\n",
              "      <th>testing</th>\n",
              "      <th>uncover</th>\n",
              "      <th>usability</th>\n",
              "      <th>use</th>\n",
              "      <th>user</th>\n",
              "      <th>website</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Topic0</th>\n",
              "      <td>1.139806</td>\n",
              "      <td>0.984566</td>\n",
              "      <td>2.190183</td>\n",
              "      <td>0.483147</td>\n",
              "      <td>1.568150</td>\n",
              "      <td>0.548083</td>\n",
              "      <td>1.087861</td>\n",
              "      <td>0.534063</td>\n",
              "      <td>0.549723</td>\n",
              "      <td>0.544324</td>\n",
              "      <td>1.047726</td>\n",
              "      <td>0.518521</td>\n",
              "      <td>0.416593</td>\n",
              "      <td>0.560087</td>\n",
              "      <td>1.167276</td>\n",
              "      <td>0.984616</td>\n",
              "      <td>0.605125</td>\n",
              "      <td>0.679546</td>\n",
              "      <td>0.487148</td>\n",
              "      <td>0.439904</td>\n",
              "      <td>1.381594</td>\n",
              "      <td>0.587897</td>\n",
              "      <td>1.002382</td>\n",
              "      <td>0.986104</td>\n",
              "      <td>0.504353</td>\n",
              "      <td>0.507652</td>\n",
              "      <td>0.455361</td>\n",
              "      <td>0.518171</td>\n",
              "      <td>0.931438</td>\n",
              "      <td>5.248646</td>\n",
              "      <td>1.049306</td>\n",
              "      <td>0.993590</td>\n",
              "      <td>0.943261</td>\n",
              "      <td>0.511151</td>\n",
              "      <td>0.490509</td>\n",
              "      <td>0.450584</td>\n",
              "      <td>0.454664</td>\n",
              "      <td>1.073053</td>\n",
              "      <td>1.608679</td>\n",
              "      <td>0.612169</td>\n",
              "      <td>1.070083</td>\n",
              "      <td>0.496328</td>\n",
              "      <td>1.113709</td>\n",
              "      <td>1.665153</td>\n",
              "      <td>0.474816</td>\n",
              "      <td>0.572495</td>\n",
              "      <td>1.595609</td>\n",
              "      <td>2.993005</td>\n",
              "      <td>6.700165</td>\n",
              "      <td>3.131821</td>\n",
              "      <td>0.509272</td>\n",
              "      <td>2.078426</td>\n",
              "      <td>0.623202</td>\n",
              "      <td>0.832566</td>\n",
              "      <td>0.971309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic1</th>\n",
              "      <td>0.547660</td>\n",
              "      <td>0.470197</td>\n",
              "      <td>0.575967</td>\n",
              "      <td>0.508790</td>\n",
              "      <td>0.493479</td>\n",
              "      <td>0.700710</td>\n",
              "      <td>0.572754</td>\n",
              "      <td>0.515575</td>\n",
              "      <td>0.567543</td>\n",
              "      <td>0.558121</td>\n",
              "      <td>1.095970</td>\n",
              "      <td>0.546411</td>\n",
              "      <td>0.477719</td>\n",
              "      <td>0.542601</td>\n",
              "      <td>0.594318</td>\n",
              "      <td>0.507788</td>\n",
              "      <td>0.699730</td>\n",
              "      <td>0.492463</td>\n",
              "      <td>0.554412</td>\n",
              "      <td>0.576324</td>\n",
              "      <td>1.083629</td>\n",
              "      <td>1.069763</td>\n",
              "      <td>0.477998</td>\n",
              "      <td>0.536230</td>\n",
              "      <td>0.559169</td>\n",
              "      <td>0.486025</td>\n",
              "      <td>0.583511</td>\n",
              "      <td>0.524998</td>\n",
              "      <td>0.614695</td>\n",
              "      <td>0.523557</td>\n",
              "      <td>0.497298</td>\n",
              "      <td>0.475597</td>\n",
              "      <td>0.507664</td>\n",
              "      <td>0.504885</td>\n",
              "      <td>0.626291</td>\n",
              "      <td>0.472171</td>\n",
              "      <td>0.569642</td>\n",
              "      <td>0.482120</td>\n",
              "      <td>0.458316</td>\n",
              "      <td>1.451126</td>\n",
              "      <td>0.543594</td>\n",
              "      <td>1.003002</td>\n",
              "      <td>1.083877</td>\n",
              "      <td>0.490950</td>\n",
              "      <td>0.496111</td>\n",
              "      <td>0.511561</td>\n",
              "      <td>0.465642</td>\n",
              "      <td>0.558685</td>\n",
              "      <td>0.455869</td>\n",
              "      <td>0.555279</td>\n",
              "      <td>0.530730</td>\n",
              "      <td>0.469238</td>\n",
              "      <td>1.863276</td>\n",
              "      <td>1.153797</td>\n",
              "      <td>0.520108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic2</th>\n",
              "      <td>0.639004</td>\n",
              "      <td>0.594850</td>\n",
              "      <td>0.510848</td>\n",
              "      <td>0.516917</td>\n",
              "      <td>0.582613</td>\n",
              "      <td>1.004794</td>\n",
              "      <td>0.527075</td>\n",
              "      <td>0.938526</td>\n",
              "      <td>0.541717</td>\n",
              "      <td>0.523572</td>\n",
              "      <td>0.572965</td>\n",
              "      <td>1.065918</td>\n",
              "      <td>0.484611</td>\n",
              "      <td>1.031324</td>\n",
              "      <td>1.166686</td>\n",
              "      <td>0.526223</td>\n",
              "      <td>1.395736</td>\n",
              "      <td>0.551563</td>\n",
              "      <td>0.535736</td>\n",
              "      <td>0.984018</td>\n",
              "      <td>0.463521</td>\n",
              "      <td>0.505100</td>\n",
              "      <td>0.502818</td>\n",
              "      <td>0.992966</td>\n",
              "      <td>0.475216</td>\n",
              "      <td>0.566461</td>\n",
              "      <td>1.515859</td>\n",
              "      <td>2.128422</td>\n",
              "      <td>2.011380</td>\n",
              "      <td>0.514178</td>\n",
              "      <td>0.585319</td>\n",
              "      <td>1.548630</td>\n",
              "      <td>0.553513</td>\n",
              "      <td>1.546103</td>\n",
              "      <td>1.006605</td>\n",
              "      <td>2.051590</td>\n",
              "      <td>0.456682</td>\n",
              "      <td>0.557933</td>\n",
              "      <td>0.500366</td>\n",
              "      <td>0.525738</td>\n",
              "      <td>0.441755</td>\n",
              "      <td>0.981919</td>\n",
              "      <td>0.513568</td>\n",
              "      <td>0.532896</td>\n",
              "      <td>1.672515</td>\n",
              "      <td>0.547082</td>\n",
              "      <td>0.552165</td>\n",
              "      <td>1.567509</td>\n",
              "      <td>0.437835</td>\n",
              "      <td>8.003119</td>\n",
              "      <td>0.940623</td>\n",
              "      <td>7.974780</td>\n",
              "      <td>0.480132</td>\n",
              "      <td>4.089680</td>\n",
              "      <td>0.498916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic3</th>\n",
              "      <td>0.505666</td>\n",
              "      <td>2.299368</td>\n",
              "      <td>0.479462</td>\n",
              "      <td>0.532915</td>\n",
              "      <td>0.556574</td>\n",
              "      <td>0.509444</td>\n",
              "      <td>0.546097</td>\n",
              "      <td>1.045039</td>\n",
              "      <td>1.018961</td>\n",
              "      <td>0.467413</td>\n",
              "      <td>0.466870</td>\n",
              "      <td>0.562087</td>\n",
              "      <td>0.520670</td>\n",
              "      <td>0.521892</td>\n",
              "      <td>0.581492</td>\n",
              "      <td>0.510120</td>\n",
              "      <td>0.532752</td>\n",
              "      <td>0.969931</td>\n",
              "      <td>0.523137</td>\n",
              "      <td>1.063862</td>\n",
              "      <td>0.739557</td>\n",
              "      <td>0.475160</td>\n",
              "      <td>0.513500</td>\n",
              "      <td>0.495423</td>\n",
              "      <td>0.574650</td>\n",
              "      <td>1.088471</td>\n",
              "      <td>0.473995</td>\n",
              "      <td>0.449335</td>\n",
              "      <td>1.072477</td>\n",
              "      <td>0.581020</td>\n",
              "      <td>0.506945</td>\n",
              "      <td>0.496190</td>\n",
              "      <td>0.472664</td>\n",
              "      <td>0.507523</td>\n",
              "      <td>0.514995</td>\n",
              "      <td>0.511064</td>\n",
              "      <td>0.535749</td>\n",
              "      <td>0.492722</td>\n",
              "      <td>0.462922</td>\n",
              "      <td>1.020224</td>\n",
              "      <td>1.026225</td>\n",
              "      <td>0.505734</td>\n",
              "      <td>1.003064</td>\n",
              "      <td>1.007037</td>\n",
              "      <td>0.544631</td>\n",
              "      <td>0.527493</td>\n",
              "      <td>0.535948</td>\n",
              "      <td>0.515595</td>\n",
              "      <td>0.476459</td>\n",
              "      <td>0.569530</td>\n",
              "      <td>0.516444</td>\n",
              "      <td>1.604425</td>\n",
              "      <td>0.457223</td>\n",
              "      <td>1.226751</td>\n",
              "      <td>1.677129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic4</th>\n",
              "      <td>0.530079</td>\n",
              "      <td>0.975950</td>\n",
              "      <td>1.133633</td>\n",
              "      <td>0.536113</td>\n",
              "      <td>0.511581</td>\n",
              "      <td>0.455249</td>\n",
              "      <td>0.570322</td>\n",
              "      <td>0.584745</td>\n",
              "      <td>0.500268</td>\n",
              "      <td>0.476989</td>\n",
              "      <td>0.448795</td>\n",
              "      <td>2.112636</td>\n",
              "      <td>0.568139</td>\n",
              "      <td>0.531053</td>\n",
              "      <td>0.462543</td>\n",
              "      <td>0.567422</td>\n",
              "      <td>0.693488</td>\n",
              "      <td>1.084039</td>\n",
              "      <td>1.649277</td>\n",
              "      <td>1.659865</td>\n",
              "      <td>0.573955</td>\n",
              "      <td>0.556540</td>\n",
              "      <td>0.522058</td>\n",
              "      <td>0.532732</td>\n",
              "      <td>0.501611</td>\n",
              "      <td>0.541220</td>\n",
              "      <td>1.171061</td>\n",
              "      <td>1.586405</td>\n",
              "      <td>1.090914</td>\n",
              "      <td>0.528488</td>\n",
              "      <td>0.509639</td>\n",
              "      <td>1.597664</td>\n",
              "      <td>1.093642</td>\n",
              "      <td>0.522770</td>\n",
              "      <td>0.491888</td>\n",
              "      <td>0.600758</td>\n",
              "      <td>0.550662</td>\n",
              "      <td>1.064079</td>\n",
              "      <td>1.028643</td>\n",
              "      <td>0.430802</td>\n",
              "      <td>0.503984</td>\n",
              "      <td>0.483078</td>\n",
              "      <td>0.504664</td>\n",
              "      <td>0.652775</td>\n",
              "      <td>0.427243</td>\n",
              "      <td>0.540332</td>\n",
              "      <td>1.037624</td>\n",
              "      <td>1.540038</td>\n",
              "      <td>1.638785</td>\n",
              "      <td>1.165346</td>\n",
              "      <td>0.565974</td>\n",
              "      <td>2.301849</td>\n",
              "      <td>0.570581</td>\n",
              "      <td>0.412520</td>\n",
              "      <td>0.604103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic5</th>\n",
              "      <td>0.555098</td>\n",
              "      <td>0.526064</td>\n",
              "      <td>0.538826</td>\n",
              "      <td>0.660407</td>\n",
              "      <td>0.592844</td>\n",
              "      <td>1.191116</td>\n",
              "      <td>0.524719</td>\n",
              "      <td>0.481331</td>\n",
              "      <td>0.524545</td>\n",
              "      <td>0.582448</td>\n",
              "      <td>0.517157</td>\n",
              "      <td>0.454560</td>\n",
              "      <td>0.457332</td>\n",
              "      <td>0.572214</td>\n",
              "      <td>0.596848</td>\n",
              "      <td>0.497454</td>\n",
              "      <td>0.536672</td>\n",
              "      <td>0.541920</td>\n",
              "      <td>1.138652</td>\n",
              "      <td>0.452496</td>\n",
              "      <td>0.487106</td>\n",
              "      <td>0.539875</td>\n",
              "      <td>0.487107</td>\n",
              "      <td>0.423961</td>\n",
              "      <td>0.570643</td>\n",
              "      <td>0.589056</td>\n",
              "      <td>0.531340</td>\n",
              "      <td>1.038274</td>\n",
              "      <td>0.519604</td>\n",
              "      <td>0.538487</td>\n",
              "      <td>0.472906</td>\n",
              "      <td>0.510134</td>\n",
              "      <td>0.507386</td>\n",
              "      <td>0.586817</td>\n",
              "      <td>0.498740</td>\n",
              "      <td>0.574608</td>\n",
              "      <td>0.515593</td>\n",
              "      <td>0.496633</td>\n",
              "      <td>0.592837</td>\n",
              "      <td>0.466821</td>\n",
              "      <td>0.492840</td>\n",
              "      <td>0.537602</td>\n",
              "      <td>0.448247</td>\n",
              "      <td>0.494317</td>\n",
              "      <td>0.537670</td>\n",
              "      <td>0.606404</td>\n",
              "      <td>0.553806</td>\n",
              "      <td>0.530014</td>\n",
              "      <td>1.088194</td>\n",
              "      <td>0.490869</td>\n",
              "      <td>0.476884</td>\n",
              "      <td>0.441128</td>\n",
              "      <td>1.019882</td>\n",
              "      <td>0.475944</td>\n",
              "      <td>0.566657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic6</th>\n",
              "      <td>0.517965</td>\n",
              "      <td>0.549793</td>\n",
              "      <td>0.554612</td>\n",
              "      <td>2.394159</td>\n",
              "      <td>0.545205</td>\n",
              "      <td>0.546548</td>\n",
              "      <td>1.028579</td>\n",
              "      <td>0.595739</td>\n",
              "      <td>0.458081</td>\n",
              "      <td>1.070803</td>\n",
              "      <td>0.464299</td>\n",
              "      <td>1.023059</td>\n",
              "      <td>1.056141</td>\n",
              "      <td>1.032798</td>\n",
              "      <td>0.557673</td>\n",
              "      <td>0.572224</td>\n",
              "      <td>0.983631</td>\n",
              "      <td>0.544126</td>\n",
              "      <td>0.561222</td>\n",
              "      <td>1.477292</td>\n",
              "      <td>2.077812</td>\n",
              "      <td>0.472133</td>\n",
              "      <td>0.525563</td>\n",
              "      <td>0.605929</td>\n",
              "      <td>0.995460</td>\n",
              "      <td>0.510799</td>\n",
              "      <td>0.485797</td>\n",
              "      <td>1.062785</td>\n",
              "      <td>0.523943</td>\n",
              "      <td>0.596464</td>\n",
              "      <td>1.101672</td>\n",
              "      <td>0.482302</td>\n",
              "      <td>0.578999</td>\n",
              "      <td>1.047208</td>\n",
              "      <td>0.986688</td>\n",
              "      <td>0.425947</td>\n",
              "      <td>1.506129</td>\n",
              "      <td>0.549999</td>\n",
              "      <td>0.510779</td>\n",
              "      <td>0.484562</td>\n",
              "      <td>0.485133</td>\n",
              "      <td>0.548278</td>\n",
              "      <td>0.507188</td>\n",
              "      <td>0.497943</td>\n",
              "      <td>0.438929</td>\n",
              "      <td>2.391113</td>\n",
              "      <td>0.441362</td>\n",
              "      <td>1.008430</td>\n",
              "      <td>0.840046</td>\n",
              "      <td>3.190452</td>\n",
              "      <td>0.500594</td>\n",
              "      <td>3.628961</td>\n",
              "      <td>1.375267</td>\n",
              "      <td>0.405481</td>\n",
              "      <td>0.526537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic7</th>\n",
              "      <td>1.173413</td>\n",
              "      <td>0.460139</td>\n",
              "      <td>0.448408</td>\n",
              "      <td>0.679089</td>\n",
              "      <td>0.491343</td>\n",
              "      <td>0.537738</td>\n",
              "      <td>0.581706</td>\n",
              "      <td>1.014023</td>\n",
              "      <td>1.104779</td>\n",
              "      <td>1.096699</td>\n",
              "      <td>0.526565</td>\n",
              "      <td>0.495411</td>\n",
              "      <td>1.083803</td>\n",
              "      <td>0.495804</td>\n",
              "      <td>0.514523</td>\n",
              "      <td>1.013358</td>\n",
              "      <td>0.506441</td>\n",
              "      <td>0.510028</td>\n",
              "      <td>0.504317</td>\n",
              "      <td>0.455877</td>\n",
              "      <td>1.009566</td>\n",
              "      <td>1.090449</td>\n",
              "      <td>1.079194</td>\n",
              "      <td>0.504547</td>\n",
              "      <td>0.490395</td>\n",
              "      <td>1.179909</td>\n",
              "      <td>0.429009</td>\n",
              "      <td>0.555645</td>\n",
              "      <td>0.547341</td>\n",
              "      <td>0.474266</td>\n",
              "      <td>0.475916</td>\n",
              "      <td>0.492655</td>\n",
              "      <td>0.555229</td>\n",
              "      <td>0.446448</td>\n",
              "      <td>0.620620</td>\n",
              "      <td>1.072312</td>\n",
              "      <td>1.658582</td>\n",
              "      <td>0.560927</td>\n",
              "      <td>1.017689</td>\n",
              "      <td>0.530139</td>\n",
              "      <td>0.457363</td>\n",
              "      <td>0.563556</td>\n",
              "      <td>0.529413</td>\n",
              "      <td>0.523405</td>\n",
              "      <td>0.529156</td>\n",
              "      <td>0.672009</td>\n",
              "      <td>0.529253</td>\n",
              "      <td>2.575372</td>\n",
              "      <td>1.227065</td>\n",
              "      <td>1.125622</td>\n",
              "      <td>1.113412</td>\n",
              "      <td>5.104880</td>\n",
              "      <td>0.720721</td>\n",
              "      <td>0.629113</td>\n",
              "      <td>0.535353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic8</th>\n",
              "      <td>0.466301</td>\n",
              "      <td>0.526269</td>\n",
              "      <td>0.493007</td>\n",
              "      <td>0.525856</td>\n",
              "      <td>0.486717</td>\n",
              "      <td>0.593598</td>\n",
              "      <td>0.531800</td>\n",
              "      <td>0.478709</td>\n",
              "      <td>1.121050</td>\n",
              "      <td>0.566696</td>\n",
              "      <td>0.652286</td>\n",
              "      <td>0.547819</td>\n",
              "      <td>0.452196</td>\n",
              "      <td>0.522897</td>\n",
              "      <td>0.578127</td>\n",
              "      <td>0.497550</td>\n",
              "      <td>0.497108</td>\n",
              "      <td>0.613017</td>\n",
              "      <td>0.488731</td>\n",
              "      <td>0.529337</td>\n",
              "      <td>0.527716</td>\n",
              "      <td>0.528786</td>\n",
              "      <td>0.489402</td>\n",
              "      <td>0.500604</td>\n",
              "      <td>0.544155</td>\n",
              "      <td>0.642449</td>\n",
              "      <td>0.543899</td>\n",
              "      <td>0.577388</td>\n",
              "      <td>0.608467</td>\n",
              "      <td>0.570796</td>\n",
              "      <td>0.551020</td>\n",
              "      <td>0.621007</td>\n",
              "      <td>0.653889</td>\n",
              "      <td>0.637401</td>\n",
              "      <td>0.565188</td>\n",
              "      <td>0.502996</td>\n",
              "      <td>0.493380</td>\n",
              "      <td>0.529800</td>\n",
              "      <td>0.511838</td>\n",
              "      <td>0.466584</td>\n",
              "      <td>0.454119</td>\n",
              "      <td>0.509140</td>\n",
              "      <td>0.585418</td>\n",
              "      <td>0.536510</td>\n",
              "      <td>0.529242</td>\n",
              "      <td>0.532998</td>\n",
              "      <td>0.522783</td>\n",
              "      <td>0.620886</td>\n",
              "      <td>0.737216</td>\n",
              "      <td>0.498784</td>\n",
              "      <td>0.562370</td>\n",
              "      <td>0.557198</td>\n",
              "      <td>0.530690</td>\n",
              "      <td>0.447720</td>\n",
              "      <td>0.531102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic9</th>\n",
              "      <td>0.518174</td>\n",
              "      <td>0.574298</td>\n",
              "      <td>0.503610</td>\n",
              "      <td>0.498045</td>\n",
              "      <td>0.551108</td>\n",
              "      <td>0.463609</td>\n",
              "      <td>0.629991</td>\n",
              "      <td>0.488556</td>\n",
              "      <td>0.458461</td>\n",
              "      <td>0.461889</td>\n",
              "      <td>0.496389</td>\n",
              "      <td>0.545977</td>\n",
              "      <td>0.537370</td>\n",
              "      <td>0.503801</td>\n",
              "      <td>0.473955</td>\n",
              "      <td>0.490313</td>\n",
              "      <td>0.542263</td>\n",
              "      <td>0.472115</td>\n",
              "      <td>0.575868</td>\n",
              "      <td>0.623026</td>\n",
              "      <td>0.521000</td>\n",
              "      <td>0.468963</td>\n",
              "      <td>0.591706</td>\n",
              "      <td>0.530394</td>\n",
              "      <td>1.248823</td>\n",
              "      <td>0.545825</td>\n",
              "      <td>0.601896</td>\n",
              "      <td>0.608300</td>\n",
              "      <td>0.516018</td>\n",
              "      <td>0.513515</td>\n",
              "      <td>0.489285</td>\n",
              "      <td>0.481829</td>\n",
              "      <td>0.612997</td>\n",
              "      <td>0.491453</td>\n",
              "      <td>0.581100</td>\n",
              "      <td>0.545900</td>\n",
              "      <td>0.670601</td>\n",
              "      <td>0.524683</td>\n",
              "      <td>0.540679</td>\n",
              "      <td>0.642238</td>\n",
              "      <td>0.522715</td>\n",
              "      <td>0.598231</td>\n",
              "      <td>0.508939</td>\n",
              "      <td>0.570608</td>\n",
              "      <td>0.490889</td>\n",
              "      <td>0.511023</td>\n",
              "      <td>0.574377</td>\n",
              "      <td>0.504982</td>\n",
              "      <td>0.524685</td>\n",
              "      <td>0.717478</td>\n",
              "      <td>0.567244</td>\n",
              "      <td>0.616869</td>\n",
              "      <td>0.764760</td>\n",
              "      <td>0.509137</td>\n",
              "      <td>0.488220</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         analyze  application    assign  ...       use      user   website\n",
              "Topic0  1.139806     0.984566  2.190183  ...  0.623202  0.832566  0.971309\n",
              "Topic1  0.547660     0.470197  0.575967  ...  1.863276  1.153797  0.520108\n",
              "Topic2  0.639004     0.594850  0.510848  ...  0.480132  4.089680  0.498916\n",
              "Topic3  0.505666     2.299368  0.479462  ...  0.457223  1.226751  1.677129\n",
              "Topic4  0.530079     0.975950  1.133633  ...  0.570581  0.412520  0.604103\n",
              "Topic5  0.555098     0.526064  0.538826  ...  1.019882  0.475944  0.566657\n",
              "Topic6  0.517965     0.549793  0.554612  ...  1.375267  0.405481  0.526537\n",
              "Topic7  1.173413     0.460139  0.448408  ...  0.720721  0.629113  0.535353\n",
              "Topic8  0.466301     0.526269  0.493007  ...  0.530690  0.447720  0.531102\n",
              "Topic9  0.518174     0.574298  0.503610  ...  0.764760  0.509137  0.488220\n",
              "\n",
              "[10 rows x 55 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W3nGF6R6C7ae"
      },
      "source": [
        "## Top N keywords of each topic"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "GZgHSHMmC-A1",
        "outputId": "2e9431bf-487b-49ed-8e72-6928a652a79d"
      },
      "source": [
        "# Show top n keywords for each topic\n",
        "def show_topics(vectorizer=vectorizer, lda_model=lda_model, n_words=20):\n",
        "    keywords = np.array(vectorizer.get_feature_names())\n",
        "    topic_keywords = []\n",
        "    for topic_weights in lda_model.components_:\n",
        "        top_keyword_locs = (-topic_weights).argsort()[:n_words]\n",
        "        topic_keywords.append(keywords.take(top_keyword_locs))\n",
        "    return topic_keywords\n",
        "\n",
        "topic_keywords = show_topics(vectorizer=vectorizer, lda_model=best_lda_model, n_words=15)\n",
        "\n",
        "# Topic - Keywords Dataframe\n",
        "df_topic_keywords = pd.DataFrame(topic_keywords)\n",
        "df_topic_keywords.columns = ['Word '+str(i) for i in range(df_topic_keywords.shape[1])]\n",
        "df_topic_keywords.index = ['Topic '+str(i) for i in range(df_topic_keywords.shape[0])]\n",
        "df_topic_keywords"
      ],
      "execution_count": 239,
      "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>Word 0</th>\n",
              "      <th>Word 1</th>\n",
              "      <th>Word 2</th>\n",
              "      <th>Word 3</th>\n",
              "      <th>Word 4</th>\n",
              "      <th>Word 5</th>\n",
              "      <th>Word 6</th>\n",
              "      <th>Word 7</th>\n",
              "      <th>Word 8</th>\n",
              "      <th>Word 9</th>\n",
              "      <th>Word 10</th>\n",
              "      <th>Word 11</th>\n",
              "      <th>Word 12</th>\n",
              "      <th>Word 13</th>\n",
              "      <th>Word 14</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Topic 0</th>\n",
              "      <td>tester</td>\n",
              "      <td>observer</td>\n",
              "      <td>testing</td>\n",
              "      <td>test</td>\n",
              "      <td>assign</td>\n",
              "      <td>usability</td>\n",
              "      <td>software</td>\n",
              "      <td>report</td>\n",
              "      <td>task</td>\n",
              "      <td>behavior</td>\n",
              "      <td>help</td>\n",
              "      <td>execute</td>\n",
              "      <td>analyze</td>\n",
              "      <td>screen</td>\n",
              "      <td>conduct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 1</th>\n",
              "      <td>use</td>\n",
              "      <td>require</td>\n",
              "      <td>user</td>\n",
              "      <td>desire</td>\n",
              "      <td>screen</td>\n",
              "      <td>help</td>\n",
              "      <td>improve</td>\n",
              "      <td>satisfaction</td>\n",
              "      <td>concentrate</td>\n",
              "      <td>follow</td>\n",
              "      <td>problem</td>\n",
              "      <td>number</td>\n",
              "      <td>execute</td>\n",
              "      <td>method</td>\n",
              "      <td>goal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 2</th>\n",
              "      <td>testing</td>\n",
              "      <td>usability</td>\n",
              "      <td>user</td>\n",
              "      <td>need</td>\n",
              "      <td>process</td>\n",
              "      <td>number</td>\n",
              "      <td>start</td>\n",
              "      <td>test</td>\n",
              "      <td>phase</td>\n",
              "      <td>practice</td>\n",
              "      <td>method</td>\n",
              "      <td>follow</td>\n",
              "      <td>execute</td>\n",
              "      <td>determine</td>\n",
              "      <td>development</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 3</th>\n",
              "      <td>application</td>\n",
              "      <td>website</td>\n",
              "      <td>usability</td>\n",
              "      <td>user</td>\n",
              "      <td>management</td>\n",
              "      <td>number</td>\n",
              "      <td>goal</td>\n",
              "      <td>cost</td>\n",
              "      <td>result</td>\n",
              "      <td>require</td>\n",
              "      <td>datum</td>\n",
              "      <td>software</td>\n",
              "      <td>screen</td>\n",
              "      <td>format</td>\n",
              "      <td>help</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 4</th>\n",
              "      <td>usability</td>\n",
              "      <td>determine</td>\n",
              "      <td>goal</td>\n",
              "      <td>functionality</td>\n",
              "      <td>tester</td>\n",
              "      <td>phase</td>\n",
              "      <td>need</td>\n",
              "      <td>test</td>\n",
              "      <td>method</td>\n",
              "      <td>testing</td>\n",
              "      <td>assign</td>\n",
              "      <td>plan</td>\n",
              "      <td>number</td>\n",
              "      <td>format</td>\n",
              "      <td>record</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 5</th>\n",
              "      <td>concentrate</td>\n",
              "      <td>functionality</td>\n",
              "      <td>tester</td>\n",
              "      <td>need</td>\n",
              "      <td>use</td>\n",
              "      <td>audience</td>\n",
              "      <td>target</td>\n",
              "      <td>execute</td>\n",
              "      <td>behavior</td>\n",
              "      <td>report</td>\n",
              "      <td>management</td>\n",
              "      <td>practice</td>\n",
              "      <td>designer</td>\n",
              "      <td>process</td>\n",
              "      <td>development</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 6</th>\n",
              "      <td>usability</td>\n",
              "      <td>testing</td>\n",
              "      <td>audience</td>\n",
              "      <td>target</td>\n",
              "      <td>help</td>\n",
              "      <td>product</td>\n",
              "      <td>goal</td>\n",
              "      <td>use</td>\n",
              "      <td>outcome</td>\n",
              "      <td>designer</td>\n",
              "      <td>need</td>\n",
              "      <td>developer</td>\n",
              "      <td>practice</td>\n",
              "      <td>development</td>\n",
              "      <td>conduct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 7</th>\n",
              "      <td>usability</td>\n",
              "      <td>test</td>\n",
              "      <td>product</td>\n",
              "      <td>tester</td>\n",
              "      <td>management</td>\n",
              "      <td>analyze</td>\n",
              "      <td>testing</td>\n",
              "      <td>uncover</td>\n",
              "      <td>datum</td>\n",
              "      <td>designer</td>\n",
              "      <td>improve</td>\n",
              "      <td>developer</td>\n",
              "      <td>lab</td>\n",
              "      <td>process</td>\n",
              "      <td>report</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 8</th>\n",
              "      <td>datum</td>\n",
              "      <td>tester</td>\n",
              "      <td>plan</td>\n",
              "      <td>desire</td>\n",
              "      <td>management</td>\n",
              "      <td>practice</td>\n",
              "      <td>phase</td>\n",
              "      <td>test</td>\n",
              "      <td>format</td>\n",
              "      <td>number</td>\n",
              "      <td>concentrate</td>\n",
              "      <td>screen</td>\n",
              "      <td>execute</td>\n",
              "      <td>need</td>\n",
              "      <td>observer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 9</th>\n",
              "      <td>make</td>\n",
              "      <td>use</td>\n",
              "      <td>testing</td>\n",
              "      <td>product</td>\n",
              "      <td>require</td>\n",
              "      <td>conduct</td>\n",
              "      <td>goal</td>\n",
              "      <td>usability</td>\n",
              "      <td>plan</td>\n",
              "      <td>need</td>\n",
              "      <td>method</td>\n",
              "      <td>satisfaction</td>\n",
              "      <td>lab</td>\n",
              "      <td>problem</td>\n",
              "      <td>functionality</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Word 0         Word 1  ...      Word 13        Word 14\n",
              "Topic 0       tester       observer  ...       screen        conduct\n",
              "Topic 1          use        require  ...       method           goal\n",
              "Topic 2      testing      usability  ...    determine    development\n",
              "Topic 3  application        website  ...       format           help\n",
              "Topic 4    usability      determine  ...       format         record\n",
              "Topic 5  concentrate  functionality  ...      process    development\n",
              "Topic 6    usability        testing  ...  development        conduct\n",
              "Topic 7    usability           test  ...      process         report\n",
              "Topic 8        datum         tester  ...         need       observer\n",
              "Topic 9         make            use  ...      problem  functionality\n",
              "\n",
              "[10 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yoge60qt4Jmq"
      },
      "source": [
        "# Predict"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bM30CH8HE0CB",
        "outputId": "b28ee724-6c86-4c87-86b3-ee3ba81e0a1f"
      },
      "source": [
        "# Define function to predict topic for a given text document.\n",
        "nlp = spacy.load('en', disable=['parser', 'ner'])\n",
        "def predict_topic(text, nlp=nlp):\n",
        "    global sent_to_words\n",
        "    global lemmatization\n",
        "# Step 1: Clean with simple_preprocess\n",
        "    mytext_2 = list(sent_to_words(text))\n",
        "# Step 2: Lemmatize\n",
        "    mytext_3 = lemmatization(mytext_2, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n",
        "# Step 3: Vectorize transform\n",
        "    mytext_4 = vectorizer.transform(mytext_3)\n",
        "# Step 4: LDA Transform\n",
        "    topic_probability_scores = best_lda_model.transform(mytext_4)\n",
        "    topic = df_topic_keywords.iloc[np.argmax(topic_probability_scores), 1:14].values.tolist()\n",
        "    \n",
        "    # Step 5: Infer Topic\n",
        "    infer_topic = df_topic_keywords.iloc[np.argmax(topic_probability_scores), -1]\n",
        "    \n",
        "    #topic_guess = df_topic_keywords.iloc[np.argmax(topic_probability_scores), Topics]\n",
        "    return infer_topic, topic, topic_probability_scores\n",
        "# Predict the topic\n",
        "mytext = [\"Very Useful in diabetes age 30. I need control sugar. thanks Good deal\"]\n",
        "infer_topic, topic, prob_scores = predict_topic(text = mytext)\n",
        "print(topic)\n",
        "print(infer_topic)"
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['observer', 'testing', 'test', 'assign', 'usability', 'software', 'report', 'task', 'behavior', 'help', 'execute', 'analyze', 'screen']\n",
            "conduct\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6l7uvluLFMmf"
      },
      "source": [
        "## Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-MGZ1WVFO7u",
        "outputId": "a3536977-53bc-4cc4-e376-84a8d50c0fa8"
      },
      "source": [
        "def apply_predict_topic(text):\n",
        " text = [text]\n",
        " infer_topic, topic, prob_scores = predict_topic(text = text)\n",
        " return(infer_topic)\n",
        "[{document: apply_predict_topic(document)} for document in documents]"
      ],
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'The goal of this testing is to satisfy users and it mainly concentrates on the following parameters of a system:': 'development'},\n",
              " {'The effectiveness of the system': 'conduct'},\n",
              " {'Is the system is easy to learn?': 'conduct'},\n",
              " {'Is the system useful and adds value to the target audience?': 'conduct'},\n",
              " {'Are Content, Color, Icons, Images used are aesthetically pleasing?': 'goal'},\n",
              " {'Efficiency': 'conduct'},\n",
              " {'Little navigation should be required to reach the desired screen or webpage, and scrollbars should be used infrequently.': 'goal'},\n",
              " {'Uniformity in the format of screen/pages in your application/website.': 'help'},\n",
              " {'Option to search within your software application or website.': 'help'},\n",
              " {'Accuracy': 'conduct'},\n",
              " {'No outdated or incorrect data like contact information/address should be present.': 'observer'},\n",
              " {'No broken links should be present.': 'conduct'},\n",
              " {'User Friendliness': 'conduct'},\n",
              " {'Controls used should be self-explanatory and must not require training to operate': 'goal'},\n",
              " {'Help should be provided for the users to understand the application/website': 'help'},\n",
              " {'Alignment with the above goals helps in effective usability testing': 'conduct'},\n",
              " {'How to do Usability Testing: Complete Process': 'report'},\n",
              " {'Usability testing process consists of the following phases': 'development'},\n",
              " {'Usability Testing Tutorial: Need, Process, Best Practice': 'development'},\n",
              " {'Planning:-  During this phase the goals of usability test are determined. Having volunteers sit in front of your application and recording their actions is not a goal. You need to determine critical functionalities and objectives of the system. You need to assign tasks to your testers, which exercise these critical functionalities. During this phase, the usability testing method, number & demographics of usability testers, test report formats are also determined': 'record'},\n",
              " {'Recruiting: During this phase, you recruit the desired number of testers as per your usability test plan. Finding testers who match your demographic (age, sex etc.) and professional ( education, job etc.) profile can take time.': 'conduct'},\n",
              " {'Usability Testing: During this phase, usability tests are actually executed.': 'development'},\n",
              " {'Data Analysis: Data from usability tests is thoroughly analyzed to derive meaningful inferences and give actionable recommendations to improve the overall usability of your product.': 'report'},\n",
              " {'Reporting: Findings of the usability test is shared with all concerned stakeholders which can include designer, developer, client, and CEO': 'report'},\n",
              " {'Methods of Usability Testing: 2 Techniques': 'development'},\n",
              " {'There are two methods available to do usability testing -': 'development'},\n",
              " {'Laboratory Usability Testing': 'development'},\n",
              " {'Remote Usability Testing': 'development'},\n",
              " {'Laboratory Usability Testing:. This testing is conducted in a separate lab room in presence of the observers. The testers are assigned tasks to execute. The role of the observer is to monitor the behavior of the testers and report the outcome of testing. The observer remains silent during the course of testing.  In this testing, both observers and testers are present in a same physical location.': 'conduct'},\n",
              " {\"Remote Usability Testing: Under this testing observers and testers are remotely located. Testers access the System Under Test, remotely and perform assigned tasks. Tester's voice , screen activity , testers facial expressions are recorded by an automated software. Observers analyze this data and report findings of the test. Example of such a software - http://silverbackapp.com/\": 'conduct'},\n",
              " {'How many users do you need ?': 'development'},\n",
              " {'Usability Testing Tutorial: Need, Process, Best Practice': 'development'},\n",
              " {'Research (Virzi, 1992 and Neilsen Landauer, 1993) indicates that 5 users are enough to uncover 80% of usability problems. Some researchers suggest other numbers.': 'development'},\n",
              " {'The truth is , the actual number of the user required depends on the complexity of the given application and your usability goals. Increase in usability participants results into increased cost , planning , participant management and data analysis.': 'help'},\n",
              " {'But as a general guideline, if you on a small budget and interested in DIY usability testing 5 is a good number to start with. If budget is not a constraint its best consult experienced professionals to determine the number of users.': 'development'},\n",
              " {'UX Testing Checklist': 'conduct'},\n",
              " {'The primary goal of this testing is to find crucial usability problems before the product is launched. Following things have to be considered to make a testing success:': 'conduct'},\n",
              " {'Start the UX testing during the early stage of design and development': 'development'},\n",
              " {\"It's a good practice to conduct usability testing on your competitor's product before you begin development. This will help you determine usability standards for your target audience\": 'conduct'},\n",
              " {'Select the appropriate users to test the system(Can be experts/non-experts users/50-50 of Experts and Non-Experts users)': 'development'},\n",
              " {'Use a bandwidth shaper . For instance , your target audience has poor network connectivity , limit network bandwidth to say 56 Kbps for your usability testers.': 'conduct'},\n",
              " {'Testers need to concentrate on  critical  & frequently used functionalities of the system.': 'development'},\n",
              " {\"Assign a single observer to each tester. This helps observer to accurately note tester's behavior. If an observer is assigned to multiple testers, results may be compromised\": 'conduct'},\n",
              " {\"Educate Designers and Developers that this testing outcomes is not a sign of failure but it's a sign of Improvement\": 'conduct'},\n",
              " {'Usability Testing Advantages': 'development'},\n",
              " {\"As with anything in life, usability testing has its merits and de-merits. Let's look at them\": 'development'},\n",
              " {'It helps uncover usability issues before the product is marketed.': 'report'},\n",
              " {'It helps improve end-user satisfaction': 'goal'},\n",
              " {'It makes your system highly effective and efficient': 'functionality'},\n",
              " {'It helps gather true feedback from your target audience who actually use your system during a usability test. You do not need to rely on \"opinions\" from random people.': 'conduct'},\n",
              " {'Usability Testing Disadvantages': 'development'},\n",
              " {'Cost is a major consideration in usability testing. It takes lots of resources to set up a Usability Test Lab. Recruiting and management of usability testers can also be expensive': 'report'},\n",
              " {'However, these costs pay themselves up in form of higher customer satisfaction, retention and repeat business. Usability testing is therefore highly recommended.': 'development'}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zdKmfN-5Fwz7"
      },
      "source": [
        "# Cluster"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_lgjf77EFwPm",
        "outputId": "1ec7d990-4eb1-4ced-a177-f6efb74f6b2e"
      },
      "source": [
        "# Construct the k-means clusters\n",
        "from sklearn.cluster import KMeans\n",
        "clusters = KMeans(n_clusters=15, random_state=100).fit_predict(lda_output)\n",
        "# Build the Singular Value Decomposition(SVD) model\n",
        "svd_model = TruncatedSVD(n_components=2)  # 2 components\n",
        "lda_output_svd = svd_model.fit_transform(lda_output)\n",
        "# X and Y axes of the plot using SVD decomposition\n",
        "x = lda_output_svd[:, 0]\n",
        "y = lda_output_svd[:, 1]\n",
        "# Weights for the 15 columns of lda_output, for each component\n",
        "print(\"Component's weights: \\n\", np.round(svd_model.components_, 2))\n",
        "# Percentage of total information in 'lda_output' explained by the two components\n",
        "print(\"Perc of Variance Explained: \\n\", np.round(svd_model.explained_variance_ratio_, 2))"
      ],
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Component's weights: \n",
            " [[ 0.06  0.06  0.98  0.06  0.04  0.05  0.09  0.07  0.05  0.05]\n",
            " [ 0.16  0.11 -0.14  0.13  0.06  0.06  0.93  0.2   0.06  0.06]]\n",
            "Perc of Variance Explained: \n",
            " [0.25 0.16]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZeeG1lqQGH3x"
      },
      "source": [
        "## Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "U_lLzrf_GJE7",
        "outputId": "50d42e95-a2c2-4d7d-9979-4302ad746308"
      },
      "source": [
        "# Plot\n",
        "plt.figure(figsize=(12, 12))\n",
        "plt.scatter(x, y, c=clusters)\n",
        "plt.xlabel('Component 2')\n",
        "plt.xlabel('Component 1')\n",
        "plt.title(\"Segregation of Topic Clusters\", )"
      ],
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Segregation of Topic Clusters')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 243
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1xIDww9wGPhx"
      },
      "source": [
        "## Get similar texts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yNVzMfi5GRWk",
        "outputId": "daf77e04-c473-4a7b-d7b4-65850233b22b"
      },
      "source": [
        "from sklearn.metrics.pairwise import euclidean_distances\n",
        "nlp = spacy.load('en', disable=['parser', 'ner'])\n",
        "def similar_documents(text, doc_topic_probs, documents = data, nlp=nlp, top_n=5, verbose=False):\n",
        "    _, topic, x  = predict_topic(text)\n",
        "    dists = euclidean_distances(x.reshape(1, -1), doc_topic_probs)[0]\n",
        "    doc_ids = np.argsort(dists)[:top_n]\n",
        "    if verbose:        \n",
        "        print(\"Topic KeyWords: \", topic)\n",
        "        print(\"Topic Prob Scores of text: \", np.round(x, 1))\n",
        "        print(\"Most Similar Doc's Probs:  \", np.round(doc_topic_probs[doc_ids], 1))\n",
        "    return doc_ids, np.take(documents, doc_ids)\n",
        "\n",
        "mytext = [\"I think they are really helpful\"]\n",
        "doc_ids, docs = similar_documents(text=mytext, doc_topic_probs=lda_output, documents = data, top_n=1, verbose=True)\n",
        "print('\\n', docs[0][:500])\n",
        "print()"
      ],
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Topic KeyWords:  ['observer', 'testing', 'test', 'assign', 'usability', 'software', 'report', 'task', 'behavior', 'help', 'execute', 'analyze', 'screen']\n",
            "Topic Prob Scores of text:  [[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]]\n",
            "Most Similar Doc's Probs:   [[0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]]\n",
            "\n",
            " user friendliness\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}