ClassificaçãoDeSentimentos.ipynb

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  "metadata": {
    "colab": {
      "name": "ClassificaçãoDeSentimentos.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vX9diSodWlih"
      },
      "source": [
        "### Importação das bibliotecas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2FjCD4o2N2XO"
      },
      "source": [
        "import numpy as np \n",
        "import math\n",
        "import re\n",
        "import pandas as pd\n",
        "from bs4 import BeautifulSoup\n",
        "from google.colab import drive\n",
        "import zipfile\n",
        "import seaborn as sns\n",
        "import spacy as sp\n",
        "import string\n",
        "import random\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OSwGeO6AXn1U"
      },
      "source": [
        "Importação para Tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "tQAXUsSxXqel",
        "outputId": "c605a8c9-d460-48d4-9dd5-80c24de90d00"
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "tf.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.5.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAONjGLYX1Wt"
      },
      "source": [
        "from tensorflow.keras import layers\n",
        "import tensorflow_datasets as tfds"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5GzhKJ7ka2dy"
      },
      "source": [
        "###Carregar arquivos para processamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ARP4AMKma5aq",
        "outputId": "822afb96-1b5c-4925-a814-49e3dbe86002"
      },
      "source": [
        "drive.mount(\"/content/drive\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gh0k66XId3U4"
      },
      "source": [
        "cols = ['sentiment', 'id', 'date', 'query', 'user', 'text']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQvHGj1veGFV"
      },
      "source": [
        "Dados para Treino:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V3Muj9p5eKst"
      },
      "source": [
        "train_data = pd.read_csv('/content/drive/MyDrive/PLN/trainingandtestdata/train.csv',\n",
        "  header=None,\n",
        "  names=cols,\n",
        "  engine=\"python\",\n",
        "  encoding=\"latin1\"\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IIFA3xCxef07",
        "outputId": "d45ff0bb-da36-4629-e92c-01846d86b2a6"
      },
      "source": [
        "train_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1600000, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "gLMK6Yp-elFD",
        "outputId": "c2baa849-49a6-4684-e545-a0f7e3687022"
      },
      "source": [
        "train_data.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sentiment</th>\n",
              "      <th>id</th>\n",
              "      <th>date</th>\n",
              "      <th>query</th>\n",
              "      <th>user</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1599995</th>\n",
              "      <td>4</td>\n",
              "      <td>2193601966</td>\n",
              "      <td>Tue Jun 16 08:40:49 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>AmandaMarie1028</td>\n",
              "      <td>Just woke up. Having no school is the best fee...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1599996</th>\n",
              "      <td>4</td>\n",
              "      <td>2193601969</td>\n",
              "      <td>Tue Jun 16 08:40:49 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>TheWDBoards</td>\n",
              "      <td>TheWDB.com - Very cool to hear old Walt interv...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1599997</th>\n",
              "      <td>4</td>\n",
              "      <td>2193601991</td>\n",
              "      <td>Tue Jun 16 08:40:49 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>bpbabe</td>\n",
              "      <td>Are you ready for your MoJo Makeover? Ask me f...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1599998</th>\n",
              "      <td>4</td>\n",
              "      <td>2193602064</td>\n",
              "      <td>Tue Jun 16 08:40:49 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>tinydiamondz</td>\n",
              "      <td>Happy 38th Birthday to my boo of alll time!!! ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1599999</th>\n",
              "      <td>4</td>\n",
              "      <td>2193602129</td>\n",
              "      <td>Tue Jun 16 08:40:50 PDT 2009</td>\n",
              "      <td>NO_QUERY</td>\n",
              "      <td>RyanTrevMorris</td>\n",
              "      <td>happy #charitytuesday @theNSPCC @SparksCharity...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sentiment  ...                                               text\n",
              "1599995          4  ...  Just woke up. Having no school is the best fee...\n",
              "1599996          4  ...  TheWDB.com - Very cool to hear old Walt interv...\n",
              "1599997          4  ...  Are you ready for your MoJo Makeover? Ask me f...\n",
              "1599998          4  ...  Happy 38th Birthday to my boo of alll time!!! ...\n",
              "1599999          4  ...  happy #charitytuesday @theNSPCC @SparksCharity...\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RD7qIv2ne8Ty",
        "outputId": "a8af7402-2e0c-46a1-82ce-835b51ded6ab"
      },
      "source": [
        "train_data.sentiment.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
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      "cell_type": "code",
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        "id": "K_fETTfjfBGo",
        "outputId": "fc34d1ba-4c8f-4c73-c927-a1656e85dafa"
      },
      "source": [
        "sns.countplot(train_data.sentiment.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ab1734890>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "tags": [],
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T525NKmzeWCO"
      },
      "source": [
        "Dados para Teste:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJFlD4IucV6C"
      },
      "source": [
        "test_data = pd.read_csv(\"/content/drive/MyDrive/PLN/trainingandtestdata/test.csv\",\n",
        "  header=None,\n",
        "  names=cols,\n",
        "  engine=\"python\",\n",
        "  encoding=\"latin1\"\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2RQWtHuseq6i",
        "outputId": "5f991efe-6c3f-4c99-f857-f73b2407445b"
      },
      "source": [
        "test_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(498, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "id": "m2Z1qmWFeuNL",
        "outputId": "9d650feb-4372-4abc-ce63-36759f18f918"
      },
      "source": [
        "test_data.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sentiment</th>\n",
              "      <th>id</th>\n",
              "      <th>date</th>\n",
              "      <th>query</th>\n",
              "      <th>user</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>493</th>\n",
              "      <td>2</td>\n",
              "      <td>14072</td>\n",
              "      <td>Sun Jun 14 04:31:43 UTC 2009</td>\n",
              "      <td>latex</td>\n",
              "      <td>proggit</td>\n",
              "      <td>Ask Programming: LaTeX or InDesign?: submitted...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494</th>\n",
              "      <td>0</td>\n",
              "      <td>14073</td>\n",
              "      <td>Sun Jun 14 04:32:17 UTC 2009</td>\n",
              "      <td>latex</td>\n",
              "      <td>sam33r</td>\n",
              "      <td>On that note, I hate Word. I hate Pages. I hat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>4</td>\n",
              "      <td>14074</td>\n",
              "      <td>Sun Jun 14 04:36:34 UTC 2009</td>\n",
              "      <td>latex</td>\n",
              "      <td>iamtheonlyjosie</td>\n",
              "      <td>Ahhh... back in a *real* text editing environm...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>0</td>\n",
              "      <td>14075</td>\n",
              "      <td>Sun Jun 14 21:36:07 UTC 2009</td>\n",
              "      <td>iran</td>\n",
              "      <td>plutopup7</td>\n",
              "      <td>Trouble in Iran, I see. Hmm. Iran. Iran so far...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>0</td>\n",
              "      <td>14076</td>\n",
              "      <td>Sun Jun 14 21:36:17 UTC 2009</td>\n",
              "      <td>iran</td>\n",
              "      <td>captain_pete</td>\n",
              "      <td>Reading the tweets coming out of Iran... The w...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sentiment  ...                                               text\n",
              "493          2  ...  Ask Programming: LaTeX or InDesign?: submitted...\n",
              "494          0  ...  On that note, I hate Word. I hate Pages. I hat...\n",
              "495          4  ...  Ahhh... back in a *real* text editing environm...\n",
              "496          0  ...  Trouble in Iran, I see. Hmm. Iran. Iran so far...\n",
              "497          0  ...  Reading the tweets coming out of Iran... The w...\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6gJJvMR2fe7o",
        "outputId": "b15079ab-0ef4-49d3-9ba1-5c7f5c71e08f"
      },
      "source": [
        "test_data.sentiment.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 0, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "lLEdupVhfh32",
        "outputId": "8efbd671-530e-4337-ae03-eba84d4234c6"
      },
      "source": [
        "sns.countplot(test_data.sentiment.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ab1752310>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vNYJqXhOfn68"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qlmm6mTKgx4y"
      },
      "source": [
        "### Limpeza dos dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k3L6zsCsfokv"
      },
      "source": [
        "#Limpeza dos dados de treinamento\n",
        "data = train_data\n",
        "data.drop(['id', 'date', 'query', 'user'], axis =1, inplace = True)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_TDQVUqhKo7"
      },
      "source": [
        "data.tail()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9JT6RAthScQ"
      },
      "source": [
        "X = data.iloc[:, 1].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMHpXcWmhWMQ"
      },
      "source": [
        "X"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tV14KAo3quhd"
      },
      "source": [
        "type(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oshe4yOwqw52"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNd_qndoqxlQ"
      },
      "source": [
        "y = data.iloc[:,0].values\n",
        "y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OiFpnDvzq5u8"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X,_, y,_= train_test_split(X, y, test_size=0.85, stratify =y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jFS1hkwQrTtP"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rwCSsfQfrWjb"
      },
      "source": [
        "y.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGWxX8orrYp7"
      },
      "source": [
        "unique, counts = np.unique(y, return_counts=True)\n",
        "unique, counts"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMhwdVw8ru4K"
      },
      "source": [
        "###Limpeza dos textos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dr7zLnUtrxB0"
      },
      "source": [
        "def clean_tweets(tweet):\n",
        "  tweet = BeautifulSoup(tweet, 'lxml').get_text()\n",
        "  tweet = re.sub(r\"@[A-Za-z0-9]+\", ' ', tweet)\n",
        "  tweet = re.sub(r\"https?://[A-Za-z0-9./]+\", ' ', tweet)\n",
        "  tweet = re.sub(r\"[^a-zA-Z.!?]\", ' ', tweet)\n",
        "  tweet = re.sub(r\" +\", ' ', tweet)\n",
        "  return tweet"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EqwTMYlBwJL0"
      },
      "source": [
        "text = \"@switchfoot http://twitpic.com/2ylzl - Awww, that's a bummer. 2 You shoulds got David Carr of Third Day to do it. ;D\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ND0cVTZPwD4M",
        "outputId": "bbd44815-9347-40a1-c167-fcdf20453f7a"
      },
      "source": [
        "text = clean_tweets(text)\n",
        "text"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "' Awww that s a bummer. You shoulds got David Carr of Third Day to do it. D'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wcFxx5P-tOG1"
      },
      "source": [
        "Limpando os textos com Stop Words e pontuações: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ar9T9egis225",
        "outputId": "22926d6c-ae59-4429-c981-ef59425c632b"
      },
      "source": [
        "nlp = sp.load ('en')\n",
        "nlp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<spacy.lang.en.English at 0x7f4ab15efa10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zsSe070dtCHw"
      },
      "source": [
        "stop_words = sp.lang.en.STOP_WORDS"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NmhUEFoDtJxH",
        "outputId": "d038bcf6-eae5-48ef-b30e-1596f5cb3189"
      },
      "source": [
        "print(stop_words)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'anywhere', 'anyone', 'this', 'wherein', 'whether', 'than', 'together', 'forty', 'just', 'will', \"'m\", 'almost', 'hereafter', 'in', 'thence', 'five', 'afterwards', 'not', 'on', 'from', \"'s\", 'are', 'often', 'throughout', 'towards', 'get', 'sixty', 'itself', 'everything', 'nevertheless', 'others', '’s', '‘ll', 'enough', 'along', 'be', 'least', 'can', 'alone', 'herself', 'besides', 'indeed', 'had', 'any', 'however', 'everyone', 'and', 'hundred', '‘re', 'after', 'never', 'two', 'several', 'using', 'but', 'ours', 'somehow', 'below', 'myself', 'the', 'seems', 'a', 'still', 'do', 'out', '‘s', 'above', 'three', 'have', 'whereafter', 'does', 'all', 'noone', 'own', 'seem', 'namely', 'into', 'yet', 'has', 'was', 'via', 'elsewhere', 'might', 'became', 'although', 'except', 'only', 'twelve', 'whereby', \"'ll\", 'perhaps', 'thus', 'last', 'latter', 'off', 'among', 'mostly', 'latterly', 'say', 'six', 'amongst', 'when', 'something', 'with', 'seemed', 'it', 'themselves', 'everywhere', 'anyhow', 'many', 'becoming', 'beyond', 'or', 'hereby', 'their', 'none', 'onto', 'of', '’re', 'n‘t', 'before', 'me', 'him', 'already', 'nine', 'at', 'down', 'being', 'himself', \"'d\", 'name', 'really', 'see', 'four', 'we', 'first', 'upon', 'please', 'by', 'is', 'may', 'which', 'meanwhile', 'sometimes', 'such', 'seeming', 'one', 'too', 'beforehand', 'she', 'why', 'formerly', 'eight', 'keep', 'therein', 'over', 'quite', 'each', 'for', 'though', 'once', 'your', '’m', 'without', 'wherever', 'whither', 'whole', 'former', 'put', 'therefore', 'i', 'serious', 'either', 'you', 'even', 'anything', '‘m', 'otherwise', 'third', 'who', \"n't\", 'make', 'through', 'her', 'as', 'moreover', 'hers', 'less', 'front', 'no', 'due', 'eleven', 'few', 'ca', 'yourself', 'where', 'always', 'another', 'behind', 'whoever', 'per', 'nobody', 'fifty', 'hereupon', 'yourselves', 'whose', 'bottom', 'nothing', 'thereupon', 'more', '’ll', 'same', '’ve', 'other', 'while', '‘ve', 'whence', 'amount', \"'re\", 'both', 'its', 're', 'again', 'very', 'someone', 'am', 'top', 'twenty', 'ever', 'back', 'further', 'full', 'toward', 'an', 'regarding', 'my', 'now', 'whom', 'ourselves', 'anyway', 'give', 'how', 'those', 'various', 'done', 'most', 'these', 'move', 'else', 'until', 'our', 'made', 'because', 'between', 'thereafter', 'thru', 'there', 'beside', 'call', 'against', 'becomes', 'cannot', 'he', '’d', 'also', 'every', 'could', 'here', 'go', 'them', \"'ve\", 'nowhere', 'under', 'then', 'unless', 'n’t', 'been', 'well', 'next', 'should', 'doing', 'herein', '‘d', 'since', 'so', 'would', 'nor', 'to', 'used', 'during', 'yours', 'neither', 'empty', 'us', 'up', 'somewhere', 'that', 'fifteen', 'about', 'must', 'some', 'did', 'whenever', 'part', 'hence', 'across', 'mine', 'sometime', 'ten', 'were', 'side', 'what', 'his', 'whatever', 'if', 'they', 'rather', 'thereby', 'show', 'much', 'whereupon', 'take', 'within', 'around', 'become', 'whereas'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Q9gD9Ah4usFT",
        "outputId": "5178dd46-3a87-4481-b941-f5b50de6395c"
      },
      "source": [
        "string.punctuation"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQeWpODKtUr6",
        "outputId": "6ac39fa9-06e3-42a5-d0d0-23d81ac062d6"
      },
      "source": [
        "#Total de Stop Words na função\n",
        "len(stop_words)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "326"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jn4REWLWtcGt"
      },
      "source": [
        "def clean_tweets2(tweet):\n",
        "  tweet = tweet.lower()\n",
        "  document = nlp(tweet)\n",
        "\n",
        "  words=[]\n",
        "  for token in document:\n",
        "    words.append(token.text)\n",
        "  \n",
        "  words = [word for word in words if word not in stop_words and word not in string.punctuation]\n",
        "  words = ' '.join([str(element) for element in words])\n",
        "\n",
        "  return words"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "uWDfPI02tt-M",
        "outputId": "c49a7f98-5128-4eda-a5af-bc0e14b25002"
      },
      "source": [
        "text2 = clean_tweets2(text)\n",
        "text2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'  awww s bummer shoulds got david carr day d'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-SlwCwfZw2sU"
      },
      "source": [
        "data_clean = [clean_tweets2(clean_tweets(tweet)) for tweet in X]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2hxE2tOJxNSf",
        "outputId": "5375422a-9833-423e-d9ac-b02108929b99"
      },
      "source": [
        "for _ in range(10):\n",
        "  print(data_clean[random.randint(0, len(data_clean) - 1)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mums nice relaxing day peter working silly hours weekend tho bit sucky hohum ...\n",
            "  problem\n",
            "saint augustine great m misssin baby\n",
            "  bad missed brotha\n",
            "  eh m drinking stay late work .. movies\n",
            "  oh poor thing booked tickets realised pat kenny s night gutted ....\n",
            "  hehehe didn t know use blog twitter suggest interesting topics ppl engage\n",
            "urgh late class\n",
            "shop\n",
            "nt feel good\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9gWVYaL8QZd"
      },
      "source": [
        "data_labels = y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6dCgcw2b8WeY"
      },
      "source": [
        "data_labels[data_labels == 4] = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ij4v8mDP8cBd",
        "outputId": "212c3127-ef5e-435d-e6d9-92eaa43d9e72"
      },
      "source": [
        "data_labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 0, 0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WdgqNq1Z8d-a",
        "outputId": "5f9a8740-8f8b-4997-bd6d-3937381f29c2"
      },
      "source": [
        "np.unique(data_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g10YYO7gvd4w"
      },
      "source": [
        "###Tokenização\n",
        "Transformando as palavras em números utilizando TensorFlow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5-VaV6G__CaM",
        "outputId": "a53b5bdc-1033-4183-ff1c-28edaf2f91b5"
      },
      "source": [
        "import math\n",
        "math.pow(2,16)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "65536.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ga0xON5dvgC-"
      },
      "source": [
        "tokenizer = tfds.deprecated.text.SubwordTextEncoder.build_from_corpus(data_clean, target_vocab_size=2**16)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VX5mxNRUHHXR",
        "outputId": "15dc2d5c-6538-48f3-dcd0-99556bb267c0"
      },
      "source": [
        "tokenizer.vocab_size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "52649"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m7_dfTDbHKN7",
        "outputId": "b2f4f98a-8194-445f-e27c-21a1166c4d01"
      },
      "source": [
        "print(tokenizer.subwords)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['  ', 's_', 'm_', 't_', ' ... ', 'good_', ' .. ', 'like_', 'got_', 'day_', 'going_', 'nt_', 'love_', 'don_', 'u_', 'work_', ' ...', 'today_', 'time_', 'know_', 'new_', 'll_', 'want_', 'think_', 'oh_', 'need_', 'lol_', 'thanks_', 'hope_', 'home_', 'night_', 've_', 'great_', 'feel_', 'd_', 'miss_', ' .... ', 'twitter_', 'wish_', 'lol', 'day', 'morning_', 'na_', 'getting_', 'gon_', 'today', 'happy_', 'bad_', 'haha_', 'sorry_', 'watching_', 'tomorrow_', 'come_', ' ..', 'nice_', 'didn_', 'fun_', 'sad_', 'way_', 'yeah_', 'sleep_', 'n_', 'work', 'hate_', 'wait_', 'hey_', 'right_', 'people_', 'better_', 'bed_', 'yes_', 'tonight_', 'having_', 'little_', 'school_', 'y_', 'week_', 'wanna_', 'long_', 'best_', 'watch_', 'thank_', 'time', 'days_', 'let_', 'feeling_', 'awesome_', 'won_', 'good', 'working_', 'e_', 'tomorrow', 'r_', 'looking_', 'o_', 'ready_', 'went_', 'ok_', 'sure_', 'night', 'ur_', 'weekend_', 'find_', 'look_', 'pretty_', 'tired_', 'man_', 'life_', 'thing_', 'maybe_', 'w_', 'trying_', 'love', 'sick_', 'guys_', 'cool_', 'phone_', 'b_', 'doesn_', 'omg_', 'finally_', 'old_', 'damn_', 'hours_', 'glad_', 'big_', 'friends_', 'thought_', 'ta_', 'looks_', 'movie_', 'home', 'right', 'house_', 'haha', 'guess_', 'yay_', 'lost_', 'things_', 'tonight', 'makes_', 'wow_', 'early_', 'amazing_', 'saw_', 'fun', 'soon_', 'tweet_', 'waiting_', 'morning', 'sad', 'bit_', 'tell_', 'start_', 'left_', 'try_', 'friend_', 'actually_', 'missed_', 'hot_', 'ya_', 'god_', 'said_', 'girl_', 'summer_', 'live_', 'sleep', 'know', 'haven_', 'sounds_', 'ing_', 'follow_', 'like', 'hi_', 'soon', 'away_', 'making_', 'found_', 'birthday_', 'thanks', ' ....', 'th_', 'sun_', 'hear_', 'baby_', 'play_', 'weather_', 'hard_', 'mom_', 'rain_', 'isn_', 'coming_', 'party_', 'help_', 'poor_', 'c_', 'a_', 'bored_', 'lot_', 'year_', 'beautiful_', 'till_', 'excited_', 'g_', 'read_', 'game_', 'aww_', 'head_', 'song_', 'listening_', 'late_', 'miss', 'missing_', 'ugh_', 'twitter', 'check_', 'music_', 'finished_', 'week', 'stuff_', 'use_', 'playing_', ' ..... ', 'woke_', 'yesterday_', 'cause_', 'stop_', 'car_', 'com_', 'totally_', 'iphone_', 'world_', 'real_', 'x_', 'wanted_', 'talk_', 'cold_', 'free_', 'sucks_', 'weekend', 'hour_', 'friday_', 'ed_', 'leave_', '. ', 'believe_', 'forward_', 'eat_', 'end_', 'buy_', 'l_', 'hair_', 'enjoy_', 'stupid_', 'thinking_', 'stay_', 'later_', 'xx', 'h_', 'sweet_', 'job_', 'gone_', 'eating_', 'better', 'i_', 'mean_', 'wants_', 'sunday_', 'sooo_', 'luck_', 'kinda_', 'okay_', 'lunch_', 'far_', 'probably_', 'food_', 'family_', 'super_', 'ill_', 'took_', 'taking_', 'cute_', 'able_', 'video_', 'bed', 'p_', 'followers_', 'k_', 'busy_', 'shit_', 'ha_', 'monday_', 'dinner_', 'lovely_', 'days', 'says_', 'hopefully_', 'half_', 'welcome_', 'dad_', 'wasn_', 'st_', 'tv_', 'coffee_', 'cuz_', 'came_', 'bad', 'seen_', 'room_', 'funny_', 'outside_', 'feels_', 'www', 'lots_', 'place_', 'ah_', 'watched_', 'reading_', 'sick', 'couldn_', 'sitting_', 'post_', 'awww_', 'times_', 'forgot_', 'sorry', 'wo_', 'run_', 'tired', 'tweets_', 'tried_', 'hello_', 'guy_', 'soo_', 'awesome', 'crazy_', 'send_', 'stuck_', 'win_', 'weeks_', 'news_', 'leaving_', 'hahaha_', 'pm_', 'class_', 'school', 'saturday_', 'life', 'kids_', 'hell_', 'beach_', 'wrong_', 'hit_', 'sucks', 'money_', 'exam_', 'dog_', 'bought_', 'seeing_', 'come', 'years_', 'way', 'idea_', 'rest_', 'meet_', 'book_', 'remember_', 'happy', 'shopping_', 'pic_', 'girls_', 'following_', 'kind_', 'needs_', 'heard_', 'talking_', 'told_', 'tho', 'computer_', 'fuck_', 'blog_', 'sunny_', 'started_', 'add_', 'face_', 'away', 'want', 'heading_', 'aw_', 'thank', 'later', 'gets_', 'btw_', 'til_', 'internet_', 'f_', 'pay_', 'true_', 'starting_', 'help', 'working', 'enjoying_', 'la_', 'cool', 'trip_', 'heart_', 'tho_', 'office_', 'hurts_', 'open_', 'ice_', 'friends', 'yay', 'ng_', 'boy_', 'think', 'hoping_', 'seriously_', 'red_', 'going', 'online_', 'great', 'loved_', 'favorite_', 'soooo_', 'goes_', 'wake_', 'awake_', 'bring_', 'mind_', '  ... 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'amazzzzi', 'amaziing_', 'amaziiii', 'amazed', 'amateur', 'amas_', 'amas', 'amarillo_', 'amaretto_', 'amandas_', 'amal', 'alyson_', 'alwys_', 'alwayz', 'alwaysz_', 'alwaysss', 'alum_', 'altr', 'alternatively_', 'also_', 'alseep_', 'alseep', 'alredy_', 'alreay', 'alreadyyy_', 'alreadd', 'alre', 'alphabeti', 'alotta_', 'alors_', 'almsot_', 'almighty', 'almi', 'almario', 'alma', 'alltime', 'alltel', 'allstar_', 'allr', 'allnighter', 'alllllllllllllllllll', 'allllllllllll', 'allllllllll', 'allllllll_', 'allllllll', 'alllll', 'alleviate', 'allergi', 'allergen', 'allegedly_', 'alla', 'alkaline_', 'alj', 'aliz', 'alix_', 'alittle', 'alistair_', 'alissa_', 'alison', 'alisha_', 'aling', 'alienware', 'alhambra_', 'alh', 'algi', 'alge', 'alg', 'alfred_', 'alfie_', 'alexia_', 'alexi', 'alexander', 'aless', 'alergies', 'alel', 'alek', 'alegria', 'alegre_', 'alee', 'alea', 'aldo', 'aldi_', 'aldean_', 'alde', 'alcoholic', 'alco_', 'alco', 'alchem', 'albuquerq', 'albumm', 'albi', 'alberts', 'alate_', 'alarming', 'alamo_', 'alama', 'alala', 'alad', 'alabang_', 'aksh', 'akl', 'akin_', 'akin', 'akhirny', 'ajk', 'aji', 'ajar', 'aiz', 'aiyo', 'aiya', 'aix', 'aisle', 'aisl', 'aise_', 'airwaves', 'airtran_', 'airt', 'airs_', 'airing', 'airfo', 'airfare', 'aircond', 'airco', 'airbrush', 'aiport_', 'aio_', 'aio', 'aint', 'ains_', 'ains', 'ainda', 'aina', 'aiming', 'ailing_', 'aileen_', 'aikido_', 'aiiii', 'aig', 'aia_', 'ahw', 'ahu_', 'aht', 'ahoo', 'ahmazing_', 'ahmad', 'ahhk', 'ahhhhhhhhhhhhhhhhh', 'ahhhhhhhhhhh_', 'ahhhhhhhhhhh', 'ahhhhhh', 'ahhahaha_', 'ahhah_', 'ahan_', 'ahahhaha', 'ahahh', 'ahahahhah', 'ahahahha_', 'ahahahahha', 'ahahahahahahahaha', 'ahahahahahaha_', 'ahahahahah', 'ahahahah', 'ahahaa', 'ahaaaa', 'ahaaa', 'aguirre_', 'ague', 'agua_', 'agricultur', 'agressive_', 'agreeing', 'agreeee', 'agra', 'agos', 'agoni', 'agog', 'agitat', 'agile_', 'agian_', 'aghhh', 'aggrevat', 'aggravating', 'aggie', 'aggi', 'aggghh', 'aggggh', 'aggggg', 'agesss_', 'agess', 'agal', 'againnn', 'agaiin', 'agai', 'agad', 'agaaaain_', 'agaaaa', 'agaa', 'aftr', 'aftie_', 'afterw', 'afterto', 'aftertaste', 'aftersun_', 'aftershocks', 'aftersh', 'afters', 'afterparty', 'afterp', 'aftermath_', 'aftere', 'afta', 'afic', 'afghan_', 'affraid', 'afflict', 'affection_', 'affecting_', 'affect', 'affairs_', 'afar_', 'afai', 'afaa', 'aest', 'aeron_', 'aerobi', 'aerial_', 'aeo', 'aeee', 'aea_', 'advocate_', 'advocat', 'advis', 'advices', 'advertising', 'adverti', 'adverse_', 'adventurin', 'adve', 'advancing_', 'adults', 'adrenalin_', 'adre', 'adoring_', 'adores_', 'adopting_', 'adolescent_', 'adol', 'adob', 'admittedly_', 'admiring_', 'admins', 'administ', 'admin', 'adli', 'adler_', 'adl', 'adkins', 'adjustment', 'adjusted_', 'adjust', 'adiml_', 'adic', 'adf', 'ader_', 'adequate', 'adele_', 'adelaide', 'aded_', 'additi', 'addison_', 'addis', 'addicts_', 'addd', 'aday', 'adapting', 'adapter', 'adapte', 'adaptation_', 'adams', 'adac', 'acual', 'actuall_', 'actresses', 'activities', 'activist_', 'activis', 'activated_', 'activated', 'actionscript_', 'actaully_', 'acrylics_', 'acronyms', 'acrobat', 'acredit', 'acquired_', 'acquire_', 'acqui', 'acquaintance', 'acqua', 'acp', 'acount_', 'acon', 'acne', 'acm', 'acl_', 'acknowledg', 'ackk', 'acity_', 'acin', 'acial', 'achievements', 'achieved', 'achh', 'acet', 'accused_', 'accurately_', 'accurate', 'acctually_', 'accross_', 'accout', 'accounted', 'accountant_', 'accountability_', 'accordingly_', 'accordi', 'accord_', 'accomplishment', 'accompa', 'accidents', 'accidentally', 'accessible_', 'accc', 'acapella_', 'academ', 'aby', 'abwe', 'abusing_', 'abusi', 'absynth', 'absorbing_', 'absor', 'absolut', 'absoloute', 'absinthe_', 'absen', 'abseil_', 'abscess_', 'abri', 'abotu', 'abos', 'aborted', 'aboot_', 'abon', 'abominati', 'abm', 'ably', 'ables', 'abiss', 'abis', 'abilities', 'abilene', 'abil', 'abf', 'aben', 'abele', 'abel', 'abee', 'abdul_', 'abdominal', 'abdo', 'abbreviation', 'abbie_', 'abbie', 'abbi', 'abbe', 'abba', 'abas', 'aayyy', 'aawwwww', 'aauu', 'aasss', 'aarrrg', 'aarrgghh_', 'aarrggh', 'aarr', 'aarons_', 'aare', 'aank', 'aane_', 'aama', 'aam', 'aall', 'aak', 'aahw', 'aahhhhh_', 'aahhh', 'aahahahaha', 'aac', 'aaayyy', 'aaawww_', 'aaaww_', 'aaaww', 'aaassseee_', 'aaasss', 'aaas', 'aaarrrgg', 'aaarrg', 'aaarr', 'aaand', 'aaan', 'aaall', 'aaai', 'aaahhhhhh', 'aaag', 'aaaawwww', 'aaaaw_', 'aaaat', 'aaaarrrg', 'aaaarr', 'aaaargh_', 'aaaar', 'aaaann', 'aaaan', 'aaaam', 'aaaall_', 'aaaal', 'aaaahhhhhh', 'aaaag', 'aaaaarrr', 'aaaaar', 'aaaaand', 'aaaaall', 'aaaaahhh', 'aaaaah', 'aaaaaay', 'aaaaaaw', 'aaaaaand', 'aaaaaah_', 'aaaaaah', 'aaaaaaan', 'aaaaaaaall', 'aaaaaaaahh', 'aaaaaaaaahhhhhh', 'aaaaaaaaaaaaaa', 'aaaaaaaaaa_', 'aaaaaaa_', '?????', '???? ... ', '??? .... ', '?? ......', '?? ....', '?? ..', '?!!', '.................', '..............', '......... ', '.... ...', '... ..', '.. ... ', '.. .. ', '.. ..', '. ... ', '. ..', '!?', '!.', '!!!!!!!!!', '!!!! ..', '!! .......', '! ...... ', '! ....', ' .................', ' ........ ......', ' ........ ..', ' ...... .... ', ' ...... ..', ' ..... .......', ' ..... .....', ' ..... ... ', ' ..... ...', ' .... ........', ' .... ......', ' .... .....', ' .... ... ... ', ' ... ?', ' ... ......', ' ... ..... ', ' ... ... ..', '  .............', '  ...........', '  ......... ', '  ........', '  ... ....']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s5fI6jSiHelT",
        "outputId": "a9ca7195-8f53-4b4d-9284-eca37e0895d9"
      },
      "source": [
        "ids =  tokenizer.encode('i am happy')\n",
        "ids"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[265, 3926, 368]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "EFyCdOgLH52F",
        "outputId": "fc23ba6c-0857-4a66-ca3f-ef65eb803c06"
      },
      "source": [
        "text = tokenizer.decode(ids)\n",
        "text"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'i am happy'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fVccIxgYIEWJ"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zzsHPN-vIG0Q"
      },
      "source": [
        "data_inputs = [tokenizer.encode(sentence) for sentence in data_clean]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ijW-6unIPna",
        "outputId": "15f69255-e404-4b1a-f9f4-15488e35945b"
      },
      "source": [
        "for _ in range(10):\n",
        "  print(data_inputs[random.randint(0, len(data_inputs) - 1)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 278, 282, 23, 7080, 45, 43, 36665]\n",
            "[402, 10, 351, 190, 46]\n",
            "[1, 199, 3152, 4932, 3, 6627, 46]\n",
            "[195, 8361, 138, 652, 1274, 3881, 17]\n",
            "[13592, 10322, 4829, 915, 160, 12, 23, 454, 63]\n",
            "[1, 553, 2258, 2069]\n",
            "[1, 342, 30655, 52507]\n",
            "[4, 61, 255, 289, 77, 47312, 219]\n",
            "[2, 6354, 1200, 2, 15527, 719, 52502]\n",
            "[1, 25, 543]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pmje32zQIkNc"
      },
      "source": [
        "###Padding (preenchimento)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QM31hhi4Ij5R",
        "outputId": "ee7ad11d-6162-4f76-cade-a2638a298aca"
      },
      "source": [
        "max_len = max([len(sentence) for sentence in data_inputs])\n",
        "max_len"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMldl2HCJ9tu"
      },
      "source": [
        "data_inputs = tf.keras.preprocessing.sequence.pad_sequences(data_inputs, value=0, padding= 'post', maxlen = max_len)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uAMH6gSPKQqJ",
        "outputId": "f9d5e24c-8ad1-4c7d-e3e4-14794a006f6c"
      },
      "source": [
        "for _ in range(10):\n",
        "  print(data_inputs[random.randint(0, len(data_inputs) - 1)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 306 1820    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0]\n",
            "[  112   478  6741    64    73   478 15803     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0]\n",
            "[    1  5103     6   122  4282   379    79 17470     6   616     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0]\n",
            "[    1   456  6549  1556 32218  1073   151     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0]\n",
            "[ 3885 10507   187  4465   303 11753     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0]\n",
            "[    1 51428     2   282  2017     8   199  3308 52493     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0]\n",
            "[1778 2921 1295  837   68  166 3286 1144   48   14    4  168    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0]\n",
            "[   1  882 1607    5  960   17    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0]\n",
            "[1798 2818  868    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "    0    0    0    0    0    0    0    0]\n",
            "[  156    16   394   922     5    22 15324  5993  3533 12849 16799 52494\n",
            "     5  1858     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IrTpFfq2MvHH"
      },
      "source": [
        "###Divisão da base de dados em treinamento e teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3Kc07PZM068"
      },
      "source": [
        "train_inputs, test_inputs, train_labels, test_labels = train_test_split(data_inputs,\n",
        "                                                                        data_labels,\n",
        "                                                                        test_size=0.3,\n",
        "                                                                        stratify = data_labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-1PFkYKZNSB4",
        "outputId": "008cc063-a1b1-4861-afb2-9a88667c583f"
      },
      "source": [
        "train_inputs[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([   1,    2, 2579, 2733, 5445,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "04z4GAbzNUi_",
        "outputId": "8e1ae509-e16b-419d-83dc-9856345a38b8"
      },
      "source": [
        "train_inputs.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(168000, 64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQ4fHX3eNlgX",
        "outputId": "0f04d70d-8752-49c5-9899-91635f39e09e"
      },
      "source": [
        "train_labels.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(168000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aLrN4w5vNp-I",
        "outputId": "b9fc80e0-332b-4675-c502-5cad13bb5fbe"
      },
      "source": [
        "test_inputs.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(72000, 64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TagjGMwhNsol",
        "outputId": "da941617-4c8a-4285-a048-d78c1f8e2ea4"
      },
      "source": [
        "test_labels.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(72000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yiFzemHGN9bL"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "###Construção do Modelo:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fjkgcQ31OCQ-"
      },
      "source": [
        "class DCNN(tf.keras.Model):\n",
        "\n",
        "  def __init__(self,\n",
        "               vocab_size,\n",
        "               emb_dim=128,\n",
        "               nb_filters=50,\n",
        "               ffn_units=512,\n",
        "               nb_classes=2,\n",
        "               dropout_rate=0.1,\n",
        "               training=True,\n",
        "               name=\"dcnn\"):\n",
        "    super(DCNN, self).__init__(name=name)\n",
        "\n",
        "#recebe a primeira camada\n",
        "    self.embedding = layers.Embedding(vocab_size, emb_dim)\n",
        "\n",
        "#recebe a segunda camada  \n",
        "    self.bigram = layers.Conv1D(filters=nb_filters, kernel_size=2, padding='same', activation='relu')\n",
        "\n",
        "#terceira camada\n",
        "    self.trigram = layers.Conv1D(filters=nb_filters, kernel_size=3, padding='same', activation='relu')\n",
        "#quarta camada\n",
        "    self.fourgram = layers.Conv1D(filters=nb_filters, kernel_size=4, padding='same', activation='relu')\n",
        "\n",
        "#camada para fazer o pooling\n",
        "    self.pool = layers.GlobalMaxPool1D()\n",
        "\n",
        "#começando a parte densa da rede neural\n",
        "    self.dense_1 = layers.Dense(units = ffn_units, activation = 'relu')\n",
        "    self.dropout = layers.Dropout(rate = dropout_rate)\n",
        "    if nb_classes == 2:\n",
        "      self.last_dense = layers.Dense(units = 1, activation = 'sigmoid')\n",
        "    else:\n",
        "      self.last_dense = layers.Dense(units = nb_classes, activation = 'softmax')\n",
        "\n",
        "  def call(self, inputs, training):\n",
        "    x = self.embedding(inputs)\n",
        "    x_1 = self.bigram(x)\n",
        "    x_1 = self.pool(x_1)\n",
        "    x_2 = self.trigram(x)\n",
        "    x_2 = self.pool(x_2)\n",
        "    x_3 = self.fourgram(x)\n",
        "    x_3 = self.pool(x_3)\n",
        "\n",
        "    merged = tf.concat([x_1, x_2, x_3], axis = -1) # (batch_size, 3 * nb_filters)\n",
        "    merged = self.dense_1(merged)\n",
        "    merged = self.dropout(merged, training)\n",
        "    output = self.last_dense(merged)\n",
        "\n",
        "    return output"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DmyYMv3NTgdD"
      },
      "source": [
        "###Configuração de parâmentros"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "886TJ6HVTjyH",
        "outputId": "538205f2-3167-41d0-cbb2-30a5cc6a7b44"
      },
      "source": [
        "vocab_size = tokenizer.vocab_size\n",
        "vocab_size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "52649"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DkH5q5HzV7QP",
        "outputId": "da50da3b-b6dd-4436-9edb-cd0974403c5c"
      },
      "source": [
        "emb_dim = 200\n",
        "nb_filters = 100\n",
        "ffn_units = 256\n",
        "batch_size = 64\n",
        "nb_classes = len(set(train_labels))\n",
        "nb_classes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j_NE4ZmOWWxF"
      },
      "source": [
        "dropout_rate = 0.2\n",
        "nb_epochs = 10"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XqzgJ9nGWHD2"
      },
      "source": [
        "###Treinamento da Rede Neural "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lLXJNWaOWKhk"
      },
      "source": [
        "Dcnn = DCNN(vocab_size=vocab_size, emb_dim=emb_dim, nb_filters=nb_filters,\n",
        "            ffn_units=ffn_units, nb_classes=nb_classes, dropout_rate=dropout_rate)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "amc0KjbwW7JW"
      },
      "source": [
        "#Compilando\n",
        "if nb_classes == 2:\n",
        "  Dcnn.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "else:\n",
        "  Dcnn.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GOT3gwOtXCVr"
      },
      "source": [
        "#Salvando o modelo caso seja perdido durante o processo no Colab\n",
        "checkpoint_path = \"./\"\n",
        "ckpt = tf.train.Checkpoint(Dcnn=Dcnn)\n",
        "ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)\n",
        "if ckpt_manager.latest_checkpoint:\n",
        "  ckpt.restore(ckpt_manager.latest_checkpoint)\n",
        "  print('Latest checkpoint restored')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "oRtZYeIcXTOC",
        "outputId": "22697901-ac37-4f3a-cb36-dbc6ce3c4e0c"
      },
      "source": [
        "history = Dcnn.fit(train_inputs, train_labels,\n",
        "                   batch_size = batch_size,\n",
        "                   epochs = nb_epochs,\n",
        "                   verbose = 1,\n",
        "                   validation_split = 0.10)\n",
        "ckpt_manager.save()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "2363/2363 [==============================] - 579s 244ms/step - loss: 0.5038 - accuracy: 0.7500 - val_loss: 0.4749 - val_accuracy: 0.7717\n",
            "Epoch 2/10\n",
            "2363/2363 [==============================] - 579s 245ms/step - loss: 0.3607 - accuracy: 0.8408 - val_loss: 0.5170 - val_accuracy: 0.7579\n",
            "Epoch 3/10\n",
            "2363/2363 [==============================] - 578s 245ms/step - loss: 0.1862 - accuracy: 0.9237 - val_loss: 0.7405 - val_accuracy: 0.7353\n",
            "Epoch 4/10\n",
            "2363/2363 [==============================] - 576s 244ms/step - loss: 0.0978 - accuracy: 0.9596 - val_loss: 0.9346 - val_accuracy: 0.7283\n",
            "Epoch 5/10\n",
            "2363/2363 [==============================] - 577s 244ms/step - loss: 0.0676 - accuracy: 0.9717 - val_loss: 1.3124 - val_accuracy: 0.7257\n",
            "Epoch 6/10\n",
            "2363/2363 [==============================] - 579s 245ms/step - loss: 0.0540 - accuracy: 0.9772 - val_loss: 1.3808 - val_accuracy: 0.7361\n",
            "Epoch 7/10\n",
            "2363/2363 [==============================] - 578s 245ms/step - loss: 0.0458 - accuracy: 0.9806 - val_loss: 1.4298 - val_accuracy: 0.7248\n",
            "Epoch 8/10\n",
            "2363/2363 [==============================] - 579s 245ms/step - loss: 0.0397 - accuracy: 0.9827 - val_loss: 1.6330 - val_accuracy: 0.7108\n",
            "Epoch 9/10\n",
            "2363/2363 [==============================] - 579s 245ms/step - loss: 0.0368 - accuracy: 0.9843 - val_loss: 1.7700 - val_accuracy: 0.7255\n",
            "Epoch 10/10\n",
            "2363/2363 [==============================] - 579s 245ms/step - loss: 0.0326 - accuracy: 0.9857 - val_loss: 1.9039 - val_accuracy: 0.7199\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'./ckpt-1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sMTgK13ZYvjl"
      },
      "source": [
        "###Avaliação do modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NzvfM4JvYyDb",
        "outputId": "bd79c762-0b63-4dda-e5ff-ace8abccef9d"
      },
      "source": [
        "results = Dcnn.evaluate(test_inputs, test_labels, batch_size=batch_size)\n",
        "print(results)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1125/1125 [==============================] - 40s 35ms/step - loss: 1.8944 - accuracy: 0.7230\n",
            "[1.8944183588027954, 0.722986102104187]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9XLBlkrqw0_c"
      },
      "source": [
        "y_pred_test = Dcnn.predict(test_inputs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_91L--9w2Ul",
        "outputId": "36d62bc6-70bf-48e6-b84e-5e0ade948fe1"
      },
      "source": [
        "y_pred_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.6900437e-11],\n",
              "       [5.9391398e-12],\n",
              "       [1.5857213e-06],\n",
              "       ...,\n",
              "       [1.8250182e-06],\n",
              "       [1.0000000e+00],\n",
              "       [5.3782672e-02]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4pq9aGBsw3cb"
      },
      "source": [
        "y_pred_test = (y_pred_test > 0.5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FL9aPx2xw42h",
        "outputId": "0d8c85ab-5bbf-4752-abf9-a702eea2c359"
      },
      "source": [
        "y_pred_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[False],\n",
              "       [False],\n",
              "       [False],\n",
              "       ...,\n",
              "       [False],\n",
              "       [ True],\n",
              "       [False]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O7c5Ie4Ew6Pu",
        "outputId": "dfef229f-e50f-4820-ce15-7ac9efd46736"
      },
      "source": [
        "test_labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SS00dzvww7w0",
        "outputId": "3383db92-ecae-40f6-a9f9-70d5e848d959"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(test_labels, y_pred_test)\n",
        "cm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[27745,  8255],\n",
              "       [11690, 24310]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "aV7zNLfBw9EX",
        "outputId": "7d72eaac-5869-49db-f457-761bb7a07e95"
      },
      "source": [
        "sns.heatmap(cm, annot=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4aa5557e90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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d14TrruOHLX+TwgGJ77qMNrMeJLZWl/ntKCVtEYmXCvpGpLu/DZT0oHJiKf3HkCillNT2AXBkCfHVQIfyzEtJW0TiZTf/GruISLTs5l9jFxGJFL0jUkQkSpS0RUQiJKK/JztdStoiEi9aaYuIRIiStohIdHiByiMiItGhlbaISHRoy5+ISJQoaYuIREi8S9pK2iISL54f76ytpC0i8RLvnK2kLSLxogeRIiJRopW2iEh0aKUtIhIlWmmLiESH51f1DCqX3sYuIrHihekfqZhZczObZmafmtlcM+sb4g3MLNvM5oc/64e4mdkjZrbAzD42s+OS7tU99J9vZt2T4m3MbE645pHwtvaUlLRFJF4Ky3Gklg/c7O6tgLbA9WbWChgATHX3TGBq+BngbCAzHL2AxyGR5IE7gROBE4A7ixJ96NMz6bqOZU1KSVtEYqWiVtrunuvuH4XzDcA8oCnQCRgWug0DLgjnnYBnPGE6UM/MGgNnAdnuvsbd1wLZQMfQVtfdp7u7A88k3atUqmmLSKyUlYx/DDNrARwLvA80cvfc0LQcaBTOmwJLky5bFmKp4stKiKekpC0iseIFZZaFtzKzXiRKGUWy3D1rmz51gDFAP3fPSy47u7ub2U7dY6ikLSKxUp6VdkjQWaW1m1kNEgl7uLuPDeEVZtbY3XNDiWNliOcAzZMubxZiOUD7beKvh3izEvqnpJq2iMSKF1raRyphJ8fTwDx3fzCpaQJQtAOkOzA+Kd4t7CJpC6wPZZTJwJlmVj88gDwTmBza8sysbRirW9K9SqWVtojESgXWtE8BugJzzGx2iA0EBgGjzawHsAToHNomAucAC4CNwFUA7r7GzP4EzAz97nb3NeH8OmAoUAuYFI6ULPHQsvJsWbUo3t8plR+lVpN2VT0F2QXlb85JvyBdipyTTks75zR97z87PN7OppW2iMRKZewe2ZUoaYtIrBSWY/dIFClpi0islPWAMeqUtEUkVpS0RUQipJL3VlQ5JW0RiRWttEVEIsRdSVtEJDIKtHtERCQ6tNIWEYkQ1bRFRCJEu0dERCJEK20RkQgpKIz3b5xW0haRWFF5REQkQgq1e0REJDq05U9EJEJUHtlBZx97bWUPIRG0Ydg1VT0FiSmVR0REIiTuu0fi/elEZLfj5TjKYmZDzGylmX2SFBtlZrPD8UXRS3/NrIWZbUpqeyLpmjZmNsfMFpjZI+Ht65hZAzPLNrP54c/6Zc1JSVtEYqXQLe0jDUOBjskBd7/U3Vu7e2tgDDA2qXlhUZu790mKPw70BDLDUXTPAcBUd88EpoafU1LSFpFYcbe0j7Lv5W8Ca0pqC6vlzsCIVPcws8ZAXXef7u4OPANcEJo7AcPC+bCkeKmUtEUkVgrLceygdsAKd5+fFGtpZrPM7A0zaxdiTYFlSX2WhRhAI3fPDefLgUZlDaoHkSISK076u0fMrBfQKymU5e5ZaV7eheKr7FzgQHdfbWZtgBfN7Ih05+LubmZlltqVtEUkVvLLseUvJOh0k/RWZlYduAhok3Sv74Hvw/mHZrYQOATIAZolXd4sxABWmFljd88NZZSVZY2t8oiIxIpjaR874HTgM3ffWvYws4ZmlhHODyLxwHFRKH/kmVnbUAfvBowPl00Auofz7knxUilpi0isVGRN28xGAO8Bh5rZMjPrEZouY/sHkKcCH4ctgC8Afdy96CHmdcBTwAJgITApxAcBZ5jZfBJ/EQwqa04qj4hIrOzgCrr4vdy7lBK/soTYGBJbAEvq/wFwZAnx1UCH8sxJSVtEYqUCdoXs0pS0RSRWCipwpb0rUtIWkViJ+dvGlLRFJF4KtdIWEYmOmP86bSVtEYkXPYgUEYmQQlN5REQkMgqqegKVTElbRGJFu0dERCJEu0dERCJEu0dERCJE5RERkQjRlj8RkQgp0EpbRCQ6tNIWEYkQJW0RkQgpxysiI0lJW0RiRSttEZEIifvX2PViXxGJlUJL/yiLmQ0xs5Vm9klS7C4zyzGz2eE4J6ntVjNbYGafm9lZSfGOIbbAzAYkxVua2fshPsrMapY1JyVtEYmVinwbOzAU6FhC/CF3bx2OiQBm1orEW9qPCNcMNrMMM8sAHgPOBloBXUJfgPvCvQ4G1gI9th1oW0raIhIrFZm03f1NYE2aQ3cCRrr79+6+GFgAnBCOBe6+yN03AyOBTmZmwGnAC+H6YcAFZQ2ipC0iseLlOHbADWb2cSif1A+xpsDSpD7LQqy0+L7AOnfP3yaekpK2iMRKeWraZtbLzD5IOnqlMcTjwE+B1kAu8EClfqBtaPeIiMRKeXaPuHsWkFWe+7v7iqJzM3sSeDn8mAM0T+raLMQoJb4aqGdm1cNqO7l/qbTSFpFYKcTTPn4MM2uc9OOFQNHOkgnAZWa2h5m1BDKBGcBMIDPsFKlJ4mHlBHd3YBpwcbi+OzC+rPG10haRWKnIL9eY2QigPbCfmS0D7gTam1lrEmXxL4DeAO4+18xGA58C+cD17l4Q7nMDMBnIAIa4+9wwRH9gpJn9GZgFPF3WnJS0RSRWKvIlCO7epYRwqYnV3e8B7ikhPhGYWEJ8EYndJWlT0haRWNHX2EVEIiTf4v3CMSVtEYmVeKdsJW0RiRmVR0REIuTHbuWLCiVtEYmVeKdsJW0RiRmVR0REIqQg5mttJW0RiRWttEVEIsS10hYRiY64r7T1W/5S+N3ffsu/Z43iydf+WWL7aRf8kqwpj/Nk9hM8PO4hDjr8oB0es0bNGtw+eCDD3voXj054mEbNGhVr379JQ1767EUu6X1xKXeQyrR8/bdcM/Q/XPSPiVz02ESGT/+81L6f5KymzR9HkT13aal90rV+4/f0fmYa5z3yMr2fmUbeps2VNlbUVfZv+atqStopTP73FG7telup7cuXruC3l/yenmf04bmHh3PTfX3TvnejZo14YPRft4uffdlZbFj3Dd3bXcWYp8bSc2DxV8b1uaM3M6bNTP9DSIXKqFaNm89szdgbzuHZa85g1IwFLFy5frt+BYWFPJz9X9r+9IBy3X/m4hX8Ydz07eJD3p7HiS0b8dKNv+LElo0Y8vanOzxWXO2kN9dUGSXtFOa8/wkb1m0otf3TDz/lm/XfADBv1mc0bLzf1rYOF57GP156hCdeHUy/e2+kWrX0/lWffOZJTHkhG4A3X3mLY09p/UPbWSexfOlylvxvyY/5OFIBGu5di8ObNACg9h41OKhhXVZu2LRdvxHvz6dDq+Y0qL1HsfjQd+ZxedYULhk8icHT5qQ97uuf53Be65YAnNe6JdM+++F35Zc21u4qH0/7iKIfnbTN7KqKnEjUnX1Zx60r4AMPbk77835B3wtvok/H6ygsLKTDhaeldZ99D9iPr7/6GoDCgkK+3fAtdevXZc+99uSyazvzzEPPVdpnkPLJWfsNn+Wu5aim+xaLr8jbyLTPltH5+IOLxd9dkMuXqzcwvOcZjOrTkXlfreXDL1amNdbqb76j4d61ANivzp6s/ua7lGPtzrwc/0TRjjyI/CPwr5IawnvWegEcVq8VTes024Fhdn3HnHQMHS89i5su+i0Ax55yLJlHZ/LYy48CsMeeNVm3ah0Adz15Bwc0P4AaNaqzf9P9eeLVwQCMG/Iik0dPKXWMbr/typinxvHdxu8q+dNIOjZ+v4XfjX6H33c8ljp71ijWdv+rs+h7+jFUq2bF4tMXLue9hcu59InJAGzanM+XazbQpsX+/ObJKWzOL2TT5nzWb9pM58dfBaDfGcdw8sGNi93HzDBLPdbuLO4PIlMmbTP7uLQmoFEpbcXeu3Z687Oi+ddZmloe1pKb7+/HrV1vJy+UUswg+9/ZPH3f9n+n3dXzbiBR077lwZu5ufMtxdpXL19FwyYNWbV8FdUyqlF779rkrc3j8GMP49Rzfk7PgT2oU7cOhe5s/m4z44dNqPwPKcVsKSjk5tHvcM5RP6FDq+bbtX/61Rr6v/AuAOs2bubt+blkVDMc6NGuFReXsCp+rueZQKKmPWH2Yv50Ydti7fvW2ZOvN2yi4d61+HrDJhrU3jPlWKcdHu+FUipRXUGnq6yVdiPgLGDtNnED3q2UGUXI/k0acteTdzCo7/3kLP6hxvjRO7O5++m7GPPUWNatXs/e9famVu1arMwp+3+F382ezpkXn8G8j+Zx6rntmP3OfwG46dc3b+3T7abfsGnjd0rYVcDd+eP4GbTcry5dTz6sxD4T+5239fwP46Zz6iFNOe3wZuxZI4PB0+ZwzlE/Ya89arAibyM1qlWjQZ09yxz3F4c25aXZi7m6XStemr2Y9oc2TTnW7my3XmmTeMtwHXefvW2Dmb1eKTPahQz8xwCOaXs0+zTYhxEznmPYA89SvUbiX9nLz73Cb/pdQd16e3PjPTcAUFBQwPXn/h9fzv+SofcPY9Dwe6lWzcjfUsCjt/8jraQ9aeSrDPj7LQx7619sWLeBe67/S6V+Rimf2V+u4uWPvyBz/322ljD+r8PRLF+/EYBLflZ6bfnkgxuzeFUe3Z5+DYC9albnnova0oCyk/bVPz+cW/79DuNmLaLJPrX56yUnV8CniacCj/dK27ySP2DcyyPy47z017Zld5LdTq0uf9zh4vzlP7kw7Zzz/JJxKcczsyHAr4CV7n5kiN0PnAdsBhYCV7n7OjNrAcwDijbvT3f3PuGaNsBQoBaJd0X2dXc3swbAKKAFiZcEd3b3bSsbxWjLn4jESgXvHhkKdNwmlg0c6e5HA/8Dbk1qW+jurcPRJyn+ONATyAxH0T0HAFPdPROYGn5OSUlbRGKlsBxHWdz9TWDNNrEp7p4ffpwOpHyIYGaNgbruPt0TpY1ngAtCcydgWDgflhQvlZK2iMRKeb7Gbma9zOyDpKNXOYe7GpiU9HNLM5tlZm+YWbsQawosS+qzLMQAGrl7bjhfTopdeUX0C6NEJFbKs+UveXtyeZnZbUA+MDyEcoED3X11qGG/aGZHlGMublb2q+SVtEUkVnbG7hEzu5LEA8oOoeSBu38PfB/OPzSzhcAhQA7FSyjNQgxghZk1dvfcUEYpc4uZyiMiEiuV/Vv+zKwjcAtwvrtvTIo3NLOMcH4QiQeOi0L5I8/M2pqZAd2A8eGyCUD3cN49KV4qrbRFJFYq8ss1ZjYCaA/sZ2bLgDtJ7BbZA8hO5OCtW/tOBe42sy1hGn3cvegh5nX8sOVvEj/UwQcBo82sB7AE6FzWnJS0RSRWKvJr7O7epYTw06X0HQOMKaXtA+DIEuKrgQ7lmZOStojESlRfbpAuJW0RiZXK/pZ3VVPSFpFYKdBKW0QkOlQeERGJEJVHREQiRCttEZEI2d3fXCMiEilxfwmCkraIxIrKIyIiEaKkLSISIdo9IiISIVppi4hEiHaPiIhESIFX5C9n3fUoaYtIrKimLSISIappi4hEiGraIiIRUhjz8ohe7CsiseLl+KcsZjbEzFaa2SdJsQZmlm1m88Of9UPczOwRM1tgZh+b2XFJ13QP/eebWfekeBszmxOueSS8+DclJW0RiZUCL0z7SMNQoOM2sQHAVHfPBKaGnwHOJvEG9kygF/A4JJI8iRcCnwicANxZlOhDn55J12071naUtEUkVgrd0z7K4u5vAmu2CXcChoXzYcAFSfFnPGE6UM/MGgNnAdnuvsbd1wLZQMfQVtfdp3tiy8szSfcqlWraIhIrO+FBZCN3zw3ny4FG4bwpsDSp37IQSxVfVkI8Ja20RSRWyrPSNrNeZvZB0tGrPGOFFfJOffKplbaIxEp5VtrungVklXOIFWbW2N1zQ4ljZYjnAM2T+jULsRyg/Tbx10O8WQn9U9JKW0RipcAL0j5+pAlA0Q6Q7sD4pHi3sIukLbA+lFEmA2eaWf3wAPJMYHJoyzOztmHXSLeke5VKK20RiZWK/Bq7mY0gsUrez8yWkdgFMggYbWY9gCVA59B9InAOsADYCFwV5rPGzP4EzAz97nb3ooeb15HYoVILmBSOlJS0RSRWKvJr7O7epZSmDiX0deD6Uu4zBBhSQvwD4MjyzElJW0RiRb8wSkQkQuL+NXYlbRGJFf3CKBGRCNFLEEREIkQ1bRGRCFFNW0QkQrTSFhGJEL1uTEQkQrTSFhGJEO0eERGJEAdXc8AAAAFUSURBVD2IFBGJEJVHREQiRN+IFBGJEK20RUQiJO41bYv730q7EjPrFV5vJLKV/ruQ8tDrxnaucr00VHYb+u9C0qakLSISIUraIiIRoqS9c6luKSXRfxeSNj2IFBGJEK20RUQiREl7JzGzjmb2uZktMLMBVT0fqXpmNsTMVprZJ1U9F4kOJe2dwMwygMeAs4FWQBcza1W1s5JdwFCgY1VPQqJFSXvnOAFY4O6L3H0zMBLoVMVzkirm7m8Ca6p6HhItSto7R1NgadLPy0JMRKRclLRFRCJESXvnyAGaJ/3cLMRERMpFSXvnmAlkmllLM6sJXAZMqOI5iUgEKWnvBO6eD9wATAbmAaPdfW7VzkqqmpmNAN4DDjWzZWbWo6rnJLs+fSNSRCRCtNIWEYkQJW0RkQhR0hYRiRAlbRGRCFHSFhGJECVtEZEIUdIWEYkQJW0RkQj5f38S39wpDDieAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YfoogTHtw-mg",
        "outputId": "3bb62a6e-408f-4f6f-d6cb-553748f4c786"
      },
      "source": [
        "history.history.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ax4IsBX6k0hy"
      },
      "source": [
        "###Gráfico referente a Perda do modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "TX2Q3MJoxBoj",
        "outputId": "769f5475-e6a9-4db7-fcfb-5a900ed3fa93"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model loss progress during training and validation')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Losses')\n",
        "plt.legend(['Training loss', 'Validation loss'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4aa637a410>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9vgTMePKk9R7"
      },
      "source": [
        "###Gráfico referente a Acurácia do modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "8nCNv8CgxHUL",
        "outputId": "0895d052-234b-4eb4-8f5e-3868cf838c92"
      },
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy progress during training and validation')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(['Training accuracy', 'Validation accuracy'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4aa4d03c90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ex_dU6OXxJfK"
      },
      "source": [
        "###Previsões"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AeZQG2bVxNut",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87e07630-2c69-48b2-d31b-c6099b084d98"
      },
      "source": [
        "text = 'i hate you'\n",
        "text = tokenizer.encode(text)\n",
        "text"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[265, 64, 3853]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MiYwRZbZxQNW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d61aa3c-2a1b-48c1-a788-69be23550a3d"
      },
      "source": [
        "Dcnn(np.array([text]), training=False).numpy()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6.8728135e-05]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    }
  ]
}