TwitterTextClassification_NLP.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM6MYWFW25/c7+YDgoyE8m4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LUCASDNORONHA/TwitterTextClassification-NLP/blob/main/TwitterTextClassification_NLP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Classificação de Textos do Twitter**\n",
        "\n",
        "O objetivo será desenvolver um modelo que seja capaz de classificar os sentimentos dos usuários do Twitter com base em seus tweets. Iremos treinar o modelo com 800 mil tweets em português separados em classes positivas, negativas e neutras.\n",
        "\n",
        "### **Sobre os dados:**\n",
        "\n",
        "Este conjunto de dados consiste em tweets em português categorizados em classes positivas, negativas e neutras para a classificação da polaridade do sentimento.\n",
        "\n",
        "Os tweets nos conjuntos de dados foram coletados principalmente do Twitter no período de 01/08/2018 a 20/10/2018.\n",
        "\n",
        "- **Fonte:** https://www.kaggle.com/augustop/portuguese-tweets-for-sentiment-analysis#TweetsNeutralHash.csv"
      ],
      "metadata": {
        "id": "DxYgGgFmgEQT"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-S3_5tkJgDt9",
        "outputId": "37062bb8-8aaf-4e0b-9757-7eba0cb450cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install spacy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy"
      ],
      "metadata": {
        "id": "Ziy6qRMCgR-A"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m spacy download pt_core_news_sm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iYSOxHeIgVVv",
        "outputId": "6a634ea1-ba7b-46d5-d0d3-833e072cc4de"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-11-18 17:39:39.472310: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2023-11-18 17:39:39.472411: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2023-11-18 17:39:39.472459: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2023-11-18 17:39:40.915345: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "Collecting pt-core-news-sm==3.6.0\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/pt_core_news_sm-3.6.0/pt_core_news_sm-3.6.0-py3-none-any.whl (13.0 MB)\n",
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            "Installing collected packages: pt-core-news-sm\n",
            "Successfully installed pt-core-news-sm-3.6.0\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('pt_core_news_sm')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import string\n",
        "import spacy\n",
        "import en_core_web_sm\n",
        "import random\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import re"
      ],
      "metadata": {
        "id": "IUVxH7EbgsnE"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Carregando Base de Dados\n"
      ],
      "metadata": {
        "id": "TyflLqxLgyLy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Base de Treinamento\n",
        "\n",
        "Negative: 0\n",
        "\n",
        "Positive: 1\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Sj8z8sWJhAgR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_treinamento = pd.read_csv('/content/Train50.csv', delimiter=';')"
      ],
      "metadata": {
        "id": "A6Q7IOamgwbR"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_treinamento.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Zc_AME8hSvT",
        "outputId": "ee8876cc-f248-4216-8f82-b55b2234ac91"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_treinamento.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ATOQfpLAhcWQ",
        "outputId": "b65c7a24-c808-4025-f9ef-ffbb94368535"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        id                                         tweet_text  \\\n",
              "8351   1049120167106686976  @coffesehun eu espero também, e eu tudo isso u...   \n",
              "31539  1049237084471525377                Acordei e não é tudo um pesadelo :(   \n",
              "27200  1049304126247657472  @BiaFreitas12 @ThaisM256 + mas com muito medo ...   \n",
              "21168  1046771738770591744  esqueci de relatar nesse site que vi o Math on...   \n",
              "31558  1049236597487652864   @jpg_untitled Sushi sim!!! Só as meias eq não :(   \n",
              "\n",
              "                           tweet_date  sentiment query_used  \n",
              "8351   Mon Oct 08 02:11:54 +0000 2018          1         :)  \n",
              "31539  Mon Oct 08 09:56:29 +0000 2018          0         :(  \n",
              "27200  Mon Oct 08 14:22:53 +0000 2018          0         :(  \n",
              "21168  Mon Oct 01 14:40:05 +0000 2018          1         :)  \n",
              "31558  Mon Oct 08 09:54:33 +0000 2018          0         :(  "
            ],
            "text/html": [
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              "      <th>id</th>\n",
              "      <th>tweet_text</th>\n",
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              "      <th>8351</th>\n",
              "      <td>1049120167106686976</td>\n",
              "      <td>@coffesehun eu espero também, e eu tudo isso u...</td>\n",
              "      <td>Mon Oct 08 02:11:54 +0000 2018</td>\n",
              "      <td>1</td>\n",
              "      <td>:)</td>\n",
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              "    <tr>\n",
              "      <th>31539</th>\n",
              "      <td>1049237084471525377</td>\n",
              "      <td>Acordei e não é tudo um pesadelo :(</td>\n",
              "      <td>Mon Oct 08 09:56:29 +0000 2018</td>\n",
              "      <td>0</td>\n",
              "      <td>:(</td>\n",
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              "    <tr>\n",
              "      <th>27200</th>\n",
              "      <td>1049304126247657472</td>\n",
              "      <td>@BiaFreitas12 @ThaisM256 + mas com muito medo ...</td>\n",
              "      <td>Mon Oct 08 14:22:53 +0000 2018</td>\n",
              "      <td>0</td>\n",
              "      <td>:(</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21168</th>\n",
              "      <td>1046771738770591744</td>\n",
              "      <td>esqueci de relatar nesse site que vi o Math on...</td>\n",
              "      <td>Mon Oct 01 14:40:05 +0000 2018</td>\n",
              "      <td>1</td>\n",
              "      <td>:)</td>\n",
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              "    <tr>\n",
              "      <th>31558</th>\n",
              "      <td>1049236597487652864</td>\n",
              "      <td>@jpg_untitled Sushi sim!!! Só as meias eq não :(</td>\n",
              "      <td>Mon Oct 08 09:54:33 +0000 2018</td>\n",
              "      <td>0</td>\n",
              "      <td>:(</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7517bc58-ae25-4713-95ab-d385a338ba6a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7517bc58-ae25-4713-95ab-d385a338ba6a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7517bc58-ae25-4713-95ab-d385a338ba6a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-df8b3f35-b903-4f2e-9938-0495b27c3db4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-df8b3f35-b903-4f2e-9938-0495b27c3db4')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-df8b3f35-b903-4f2e-9938-0495b27c3db4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_treinamento['sentiment'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3Iq5zKLhsDI",
        "outputId": "8ad47781-79d4-416d-981c-fed3703f4820"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    25000\n",
              "0    25000\n",
              "Name: sentiment, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "values_counts = base_treinamento['sentiment'].value_counts()\n",
        "\n",
        "sns.countplot(x = 'sentiment', data=base_treinamento);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "11nnE5pHhfyB",
        "outputId": "12353ab8-e7b1-48fa-d166-cde293e26a6e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_treinamento.drop(['id', 'tweet_date', 'query_used'], axis = 1, inplace=True)"
      ],
      "metadata": {
        "id": "EvAaPThxhj8_"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_treinamento.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tKNo3vBWixzi",
        "outputId": "5f106c3e-0116-455c-e17d-502e934dc073"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          tweet_text  sentiment\n",
              "0  @Laranjito76 A pessoa certa para isso seria o ...          1\n",
              "1  @behin_d_curtain Para mim, é precisamente o co...          1\n",
              "2  Vou fazer um video hoje... estou pensando em f...          1\n",
              "3  aaaaaaaa amei tanto essas polaroids, nem sei e...          1\n",
              "4  Valoriza o coração do menininho que vc tem. El...          1"
            ],
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              "      <td>@Laranjito76 A pessoa certa para isso seria o ...</td>\n",
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              "      <td>Vou fazer um video hoje... estou pensando em f...</td>\n",
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              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>Valoriza o coração do menininho que vc tem. El...</td>\n",
              "      <td>1</td>\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-552dd282-305b-4bec-a119-ef978e8c540f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-552dd282-305b-4bec-a119-ef978e8c540f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3a138ec0-34ec-454e-990f-8cc481b0a1e6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3a138ec0-34ec-454e-990f-8cc481b0a1e6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3a138ec0-34ec-454e-990f-8cc481b0a1e6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(pd.isnull(base_treinamento));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "7fKLnUpFi1JZ",
        "outputId": "94a72fe5-8fee-41ea-c0bb-337aa3737b67"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Base de Teste"
      ],
      "metadata": {
        "id": "NtF_WfGQi69D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_teste = pd.read_csv('/content/Test.csv', delimiter=';')"
      ],
      "metadata": {
        "id": "XIqEv8_Ci11I"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_teste.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "MEMnfpAWi_p1",
        "outputId": "69fdf0c3-a693-4459-84ef-1cbfb66a83ef"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    id                                         tweet_text  \\\n",
              "0  1029536486021099522                   @Gazo1a Nossa! Muito obrigada :)   \n",
              "1  1029536496368406528             @BerzGamer vai pa puta que te pariu :)   \n",
              "2  1029536531655131137  QUER MAIS DESCONTOS? (14/08) ⭐⭐⭐⭐⭐ 🌐 Confira n...   \n",
              "3  1029536560117678081  EU VOU PEGAR VCS, ME AJUDEM GALERA, PELO AMOR ...   \n",
              "4  1029536605852377088  Estávamos em casa do Zé e eu estava a morrer d...   \n",
              "\n",
              "                       tweet_date  sentiment query_used  \n",
              "0  Wed Aug 15 01:13:20 +0000 2018          1         :)  \n",
              "1  Wed Aug 15 01:13:23 +0000 2018          1         :)  \n",
              "2  Wed Aug 15 01:13:31 +0000 2018          1         :)  \n",
              "3  Wed Aug 15 01:13:38 +0000 2018          1         :)  \n",
              "4  Wed Aug 15 01:13:49 +0000 2018          1         :)  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fe716d00-7548-4d10-b663-1ae54a4acede\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tweet_text</th>\n",
              "      <th>tweet_date</th>\n",
              "      <th>sentiment</th>\n",
              "      <th>query_used</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1029536486021099522</td>\n",
              "      <td>@Gazo1a Nossa! Muito obrigada :)</td>\n",
              "      <td>Wed Aug 15 01:13:20 +0000 2018</td>\n",
              "      <td>1</td>\n",
              "      <td>:)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1029536496368406528</td>\n",
              "      <td>@BerzGamer vai pa puta que te pariu :)</td>\n",
              "      <td>Wed Aug 15 01:13:23 +0000 2018</td>\n",
              "      <td>1</td>\n",
              "      <td>:)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1029536531655131137</td>\n",
              "      <td>QUER MAIS DESCONTOS? (14/08) ⭐⭐⭐⭐⭐ 🌐 Confira n...</td>\n",
              "      <td>Wed Aug 15 01:13:31 +0000 2018</td>\n",
              "      <td>1</td>\n",
              "      <td>:)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1029536560117678081</td>\n",
              "      <td>EU VOU PEGAR VCS, ME AJUDEM GALERA, PELO AMOR ...</td>\n",
              "      <td>Wed Aug 15 01:13:38 +0000 2018</td>\n",
              "      <td>1</td>\n",
              "      <td>:)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1029536605852377088</td>\n",
              "      <td>Estávamos em casa do Zé e eu estava a morrer d...</td>\n",
              "      <td>Wed Aug 15 01:13:49 +0000 2018</td>\n",
              "      <td>1</td>\n",
              "      <td>:)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe716d00-7548-4d10-b663-1ae54a4acede')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fe716d00-7548-4d10-b663-1ae54a4acede button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fe716d00-7548-4d10-b663-1ae54a4acede');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-104bc8eb-4c7d-4254-80b4-9f6f4eb0fea7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-104bc8eb-4c7d-4254-80b4-9f6f4eb0fea7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-104bc8eb-4c7d-4254-80b4-9f6f4eb0fea7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_teste.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DR3QDd6VjCVc",
        "outputId": "0a183ef0-f6eb-49cb-8a0f-ddba4a23206a"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_teste.drop(['id', 'tweet_date', 'query_used'], axis = 1, inplace=True)"
      ],
      "metadata": {
        "id": "np1EbcYKjEpT"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_teste.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ksl10o_yjHFl",
        "outputId": "7784bcbd-5d5d-4f77-fe70-afb4835653b2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          tweet_text  sentiment\n",
              "0                   @Gazo1a Nossa! Muito obrigada :)          1\n",
              "1             @BerzGamer vai pa puta que te pariu :)          1\n",
              "2  QUER MAIS DESCONTOS? (14/08) ⭐⭐⭐⭐⭐ 🌐 Confira n...          1\n",
              "3  EU VOU PEGAR VCS, ME AJUDEM GALERA, PELO AMOR ...          1\n",
              "4  Estávamos em casa do Zé e eu estava a morrer d...          1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b01825b3-c609-4698-81bf-fd213e03ea87\" class=\"colab-df-container\">\n",
              "    <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>tweet_text</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>@Gazo1a Nossa! Muito obrigada :)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>@BerzGamer vai pa puta que te pariu :)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>QUER MAIS DESCONTOS? (14/08) ⭐⭐⭐⭐⭐ 🌐 Confira n...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>EU VOU PEGAR VCS, ME AJUDEM GALERA, PELO AMOR ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Estávamos em casa do Zé e eu estava a morrer d...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b01825b3-c609-4698-81bf-fd213e03ea87')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b01825b3-c609-4698-81bf-fd213e03ea87 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b01825b3-c609-4698-81bf-fd213e03ea87');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c38c2b6b-b526-4964-ad7b-7018e71f8cdb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c38c2b6b-b526-4964-ad7b-7018e71f8cdb')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c38c2b6b-b526-4964-ad7b-7018e71f8cdb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(pd.isnull(base_teste));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "YngL_ECtjLbw",
        "outputId": "1e6557d1-72c4-42e2-a6ee-a7a2082c9600"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Funções: Pré-Processamento de Textos\n",
        "\n",
        "- Letras minúsculas\n",
        "\n",
        "- Nome do usuário (@)\n",
        "\n",
        "- URLs\n",
        "\n",
        "- Espaços em branco\n",
        "\n",
        "- Emoticons\n",
        "\n",
        "- Stop words\n",
        "\n",
        "- Lematização\n",
        "\n",
        "- Pontuações"
      ],
      "metadata": {
        "id": "pbsLEhfWjOoa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pln = en_core_web_sm.load()\n",
        "pln"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "inVGVNCOjbAh",
        "outputId": "26c0b374-e9af-411e-a8fb-9d017227501a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<spacy.lang.en.English at 0x7825558efbe0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from spacy.lang.pt.stop_words import STOP_WORDS"
      ],
      "metadata": {
        "id": "Br4jeVbSjdhg"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stop_words = STOP_WORDS"
      ],
      "metadata": {
        "id": "5NJYQ7cfjeiq"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocessamento(texto):\n",
        "    # Letras minúsculas\n",
        "    texto = texto.lower()\n",
        "\n",
        "    # Nome do usuário\n",
        "    texto = re.sub(r\"@[A-Za-z0-9$-_@.&+]+\", ' ', texto)\n",
        "\n",
        "    # URLs\n",
        "    texto = re.sub(r\"https?://[A-Za-z0-9./]+\", ' ', texto)\n",
        "\n",
        "    # Espaços em branco\n",
        "    texto = re.sub(r\" +\", ' ', texto)\n",
        "\n",
        "    # Emoticons\n",
        "    lista_emocoes = {':)': 'emocaopositiva',\n",
        "                    ':d': 'emocaopositiva',\n",
        "                    ':(': 'emocaonegativa'}\n",
        "    for emocao in lista_emocoes:\n",
        "        texto = texto.replace(emocao, lista_emocoes[emocao])\n",
        "\n",
        "    # Lematização\n",
        "    documento = pln(texto)  # Certifique-se de que 'pln' está definido anteriormente\n",
        "\n",
        "    lista = []\n",
        "    for token in documento:\n",
        "        lista.append(token.lemma_)\n",
        "\n",
        "    # Stop words e pontuações\n",
        "    lista = [palavra for palavra in lista if palavra not in STOP_WORDS and palavra not in string.punctuation]\n",
        "    lista = ' '.join([str(elemento) for elemento in lista if not elemento.isdigit()])\n",
        "\n",
        "    return lista"
      ],
      "metadata": {
        "id": "hk6pLbU1ji1y"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pré-Processamento da Base de Dados\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "NE8PUqqljmw4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Limpeza dos Textos"
      ],
      "metadata": {
        "id": "ngShi14ljrgQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_treinamento.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "2uFPxTA5jqRw",
        "outputId": "78685b03-e3de-4cf3-9b27-2bab651e6906"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          tweet_text  sentiment\n",
              "0  @Laranjito76 A pessoa certa para isso seria o ...          1\n",
              "1  @behin_d_curtain Para mim, é precisamente o co...          1\n",
              "2  Vou fazer um video hoje... estou pensando em f...          1\n",
              "3  aaaaaaaa amei tanto essas polaroids, nem sei e...          1\n",
              "4  Valoriza o coração do menininho que vc tem. El...          1\n",
              "5       @KingJokerLeto mas amiga eu to aqui ainda :)          1\n",
              "6                   @RivasJairo Bravo, Jairo!!! :)))          1\n",
              "7  @inquilinasls @Spotify socorro ksoaksosks acho...          1\n",
              "8  @chimekarlla gosto muito de fazenda :) , mas m...          1\n",
              "9                            Amanha vou ver venom :D          1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5d13bb8d-469b-448c-b1cb-26b94542baea\" class=\"colab-df-container\">\n",
              "    <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>tweet_text</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>@Laranjito76 A pessoa certa para isso seria o ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>@behin_d_curtain Para mim, é precisamente o co...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Vou fazer um video hoje... estou pensando em f...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>aaaaaaaa amei tanto essas polaroids, nem sei e...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Valoriza o coração do menininho que vc tem. El...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>@KingJokerLeto mas amiga eu to aqui ainda :)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>@RivasJairo Bravo, Jairo!!! :)))</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>@inquilinasls @Spotify socorro ksoaksosks acho...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>@chimekarlla gosto muito de fazenda :) , mas m...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Amanha vou ver venom :D</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d13bb8d-469b-448c-b1cb-26b94542baea')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5d13bb8d-469b-448c-b1cb-26b94542baea button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5d13bb8d-469b-448c-b1cb-26b94542baea');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a353a3f6-af5b-4aa2-9032-06b73be46190\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a353a3f6-af5b-4aa2-9032-06b73be46190')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a353a3f6-af5b-4aa2-9032-06b73be46190 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_treinamento['tweet_text'] = base_treinamento['tweet_text'].apply(preprocessamento)"
      ],
      "metadata": {
        "id": "dH5BYmB5jy4o"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_teste.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "wqXdQ02hj1O4",
        "outputId": "43bbf52b-d96e-4dc5-ea19-4da3e1806cc8"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          tweet_text  sentiment\n",
              "0                   @Gazo1a Nossa! Muito obrigada :)          1\n",
              "1             @BerzGamer vai pa puta que te pariu :)          1\n",
              "2  QUER MAIS DESCONTOS? (14/08) ⭐⭐⭐⭐⭐ 🌐 Confira n...          1\n",
              "3  EU VOU PEGAR VCS, ME AJUDEM GALERA, PELO AMOR ...          1\n",
              "4  Estávamos em casa do Zé e eu estava a morrer d...          1\n",
              "5                 Precisa :) https://t.co/DcLGDHbjT0          1\n",
              "6  @Jeniabreu07 mas por acaso adoro beijos e abra...          1\n",
              "7                       Solteiro s sozinho sempre :)          1\n",
              "8  Lindezas Cortador kit Mário coleção Bia Cravol...          1\n",
              "9                        @otphurts Claro que sim :))          1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-280a9402-86df-4cb9-afee-1250575a7b87\" class=\"colab-df-container\">\n",
              "    <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>tweet_text</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>@Gazo1a Nossa! Muito obrigada :)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>@BerzGamer vai pa puta que te pariu :)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>QUER MAIS DESCONTOS? (14/08) ⭐⭐⭐⭐⭐ 🌐 Confira n...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>EU VOU PEGAR VCS, ME AJUDEM GALERA, PELO AMOR ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Estávamos em casa do Zé e eu estava a morrer d...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Precisa :) https://t.co/DcLGDHbjT0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>@Jeniabreu07 mas por acaso adoro beijos e abra...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Solteiro s sozinho sempre :)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Lindezas Cortador kit Mário coleção Bia Cravol...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>@otphurts Claro que sim :))</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-280a9402-86df-4cb9-afee-1250575a7b87')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-280a9402-86df-4cb9-afee-1250575a7b87 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-280a9402-86df-4cb9-afee-1250575a7b87');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c884ea30-bb78-4360-a03e-b839334a952b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c884ea30-bb78-4360-a03e-b839334a952b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c884ea30-bb78-4360-a03e-b839334a952b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tratamento de Classe"
      ],
      "metadata": {
        "id": "hrNhLfY_j8wY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "exemplo_base_dados = [[\"este trabalho é agradável\", {\"POSITIVO\": True, \"NEGATIVO\": False}],\n",
        "                      [\"este lugar continua assustador\", {\"POSITIVO\": False, \"NEGATIVO\": True}]]"
      ],
      "metadata": {
        "id": "5RLSGROPj7Pb"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_dados_treinamento_final = []\n",
        "for texto, emocao in zip(base_treinamento['tweet_text'], base_treinamento['sentiment']):\n",
        "  if emocao == 1:\n",
        "    dic = ({'POSITIVO': True, 'NEGATIVO': False})\n",
        "  elif emocao == 0:\n",
        "    dic = ({'POSITIVO': False, 'NEGATIVO': True})\n",
        "\n",
        "  base_dados_treinamento_final.append([texto, dic.copy()])"
      ],
      "metadata": {
        "id": "s6RPR8LikHXk"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(base_dados_treinamento_final)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aWmPuitgkJUI",
        "outputId": "5f7f6f60-c324-445a-bcef-784adfa9c178"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50000"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_dados_treinamento_final[10:15]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-xdjUnXlkLNh",
        "outputId": "e1f86936-052c-4cae-9109-68246f4fd585"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['simplesmente I deixam chateada coisas muitooo I iludir emocaopositiva',\n",
              "  {'POSITIVO': True, 'NEGATIVO': False}],\n",
              " ['começa rotina perfeitamente consolidar escola condução :p',\n",
              "  {'POSITIVO': True, 'NEGATIVO': False}],\n",
              " ['amigo apaixonado vc — recíproco emocaopositiva',\n",
              "  {'POSITIVO': True, 'NEGATIVO': False}],\n",
              " ['  gajo bacano vê I aleijas espero silva sejam felize emocaopositiva',\n",
              "  {'POSITIVO': True, 'NEGATIVO': False}],\n",
              " ['tou video cabrinhas bebés emocaopositiva',\n",
              "  {'POSITIVO': True, 'NEGATIVO': False}]]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_dados_treinamento_final[45000:45005]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zSRNGuv2kNkH",
        "outputId": "3a84b1c8-f786-47b0-89dc-60377e9d70f9"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['menino pijama listrado filme triste mundo estrutura pra assistir rever to pensando nisso emocaonegativa',\n",
              "  {'POSITIVO': False, 'NEGATIVO': True}],\n",
              " ['pra ido vídeo luísa raiz poliana infelizmente twitter tá ruim amanhã tento postar emocaonegativa asaventurasdepoliana099',\n",
              "  {'POSITIVO': False, 'NEGATIVO': True}],\n",
              " ['to crise abstinência stranger emocaonegativa — tbm :/ to tentando',\n",
              "  {'POSITIVO': False, 'NEGATIVO': True}],\n",
              " ['  tinha esquecido olhar pra pagar mensalidade turma pro curso emocaonegativa',\n",
              "  {'POSITIVO': False, 'NEGATIVO': True}],\n",
              " ['comer doce comi monte hoje emocaonegativa maldição',\n",
              "  {'POSITIVO': False, 'NEGATIVO': True}]]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Criação do Classificador"
      ],
      "metadata": {
        "id": "pp2ZgNpCkQEY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "modelo = spacy.blank('pt')\n",
        "categorias = modelo.add_pipe(\"textcat\")\n",
        "categorias.add_label(\"POSITIVO\")\n",
        "categorias.add_label(\"NEGATIVO\")\n",
        "historico = []"
      ],
      "metadata": {
        "id": "eDOgMHf8kRzj"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from spacy.training import Example"
      ],
      "metadata": {
        "id": "KEvIrp_AkVDa"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modelo.begin_training()\n",
        "for epoca in range(5):\n",
        "  random.shuffle(base_dados_treinamento_final)\n",
        "  losses = {}\n",
        "  for batch in spacy.util.minibatch(base_dados_treinamento_final, 512):\n",
        "    textos = [modelo(texto) for texto, entities in batch]\n",
        "    annotations = [{'cats': entities} for texto, entities in batch]\n",
        "    examples = [Example.from_dict(doc, annotation) for doc, annotation in zip(\n",
        "        textos, annotations\n",
        "      )]\n",
        "    modelo.update(examples, losses=losses)\n",
        "    historico.append(losses)\n",
        "  if epoca % 5 == 0:\n",
        "    print(losses)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xt8sApPlkXOH",
        "outputId": "60ea9d05-2c81-4260-de14-7e06f63ce2a7"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'textcat': 1.72788427091713}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "historico_loss = []\n",
        "for i in historico:\n",
        "  historico_loss.append(i.get('textcat'))"
      ],
      "metadata": {
        "id": "n6GJhhTlkZfT"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "historico_loss = np.array(historico_loss)\n",
        "historico_loss"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1QNdr_TNkbVJ",
        "outputId": "84cd343a-1ae3-4961-bb2b-975f0f4aeec3"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 1.72788427, 1.72788427,\n",
              "       1.72788427, 1.72788427, 1.72788427, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.05861702, 0.05861702, 0.05861702, 0.05861702,\n",
              "       0.05861702, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.02766537,\n",
              "       0.02766537, 0.02766537, 0.02766537, 0.02766537, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00682097, 0.00682097, 0.00682097,\n",
              "       0.00682097, 0.00682097, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432,\n",
              "       0.00330432, 0.00330432, 0.00330432, 0.00330432, 0.00330432])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(historico_loss)\n",
        "plt.title('Progressão do erro')\n",
        "plt.xlabel('Batches')\n",
        "plt.ylabel('Erro')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "qfvRELYxkekG",
        "outputId": "ca135bff-f636-4961-f0d6-8cc4aeb50ec5"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Erro')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modelo.to_disk(\"modelo\")"
      ],
      "metadata": {
        "id": "WmOOJ0AgkghZ"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Teste"
      ],
      "metadata": {
        "id": "6RM_T7Xiki4Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "modelo_carregado = spacy.load('modelo')\n",
        "modelo_carregado"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LdmSyrpoklCx",
        "outputId": "65222c75-2711-4af6-c0aa-c0398c31347b"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<spacy.lang.pt.Portuguese at 0x78254eed1a80>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Texto positivo"
      ],
      "metadata": {
        "id": "rAt9jg1ykoRQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "texto_positivo = base_teste['tweet_text'][21]\n",
        "texto_positivo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "id": "mh2X8lqNkp7Y",
        "outputId": "6a0401a5-59ad-4f05-94a9-1c59aea38009"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'@duda_tn fico feliz sabendo q é recíproco :))'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "previsao = modelo_carregado(texto_positivo)\n",
        "previsao"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "em6nuvBKkuQb",
        "outputId": "127aeddc-3a3c-4a4a-8e9b-84162c6235c4"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "@duda_tn fico feliz sabendo q é recíproco :))"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "previsao.cats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0iEUQhPwlNZo",
        "outputId": "0bd7cb82-0e36-45d0-f276-98b6d7c13064"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'POSITIVO': 0.021774042397737503, 'NEGATIVO': 0.9782258868217468}"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "texto_positivo = 'eu gosto muito de você'\n",
        "texto_positivo = preprocessamento(texto_positivo)\n",
        "texto_positivo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "id": "PS_u_ydjlP0j",
        "outputId": "f4fa54e8-812e-40a4-87cc-2e5b0751e5c8"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'gosto'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modelo_carregado(texto_positivo).cats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B_UiwyMtlRjj",
        "outputId": "813e1fda-58a6-4179-a386-82ea3d0471cd"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'POSITIVO': 0.9987525939941406, 'NEGATIVO': 0.0012473980896174908}"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Texto negativo"
      ],
      "metadata": {
        "id": "Q_OAWtTPlUjo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_teste['tweet_text'][4000]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "id": "5Fq1GX4ElV4w",
        "outputId": "82845ac8-d457-4527-cfd8-3836fcbe9071"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Tô bem triste do filme da Mulan não ter o Mushu :('"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "texto_negativo = base_teste['tweet_text'][4000]\n",
        "previsao = modelo_carregado(texto_negativo)\n",
        "previsao.cats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNtx_fZIlZJK",
        "outputId": "77d6843e-6f49-4bb0-b225-9692f835d29f"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'POSITIVO': 0.9857467412948608, 'NEGATIVO': 0.014253324829041958}"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Avaliação do modelo"
      ],
      "metadata": {
        "id": "bYhsJMwMlcZj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "previsoes = []\n",
        "for texto in base_teste['tweet_text']:\n",
        "  previsao = modelo_carregado(texto)\n",
        "  previsoes.append(previsao.cats)"
      ],
      "metadata": {
        "id": "bYrzPhnFlfBh"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "previsoes_final = []\n",
        "for previsao in previsoes:\n",
        "  if previsao['POSITIVO'] > previsao['NEGATIVO']:\n",
        "    previsoes_final.append(1)\n",
        "  else:\n",
        "    previsoes_final.append(0)\n",
        "\n",
        "previsoes_final = np.array(previsoes_final)"
      ],
      "metadata": {
        "id": "BrnQul7DlhM5"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "respostas_reais = base_teste['sentiment'].values"
      ],
      "metadata": {
        "id": "z-CVhu3slkPa"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(respostas_reais, previsoes_final)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_1uPANTglmFR",
        "outputId": "a06a5379-fc76-4355-d0ca-926fbb6ff950"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3832"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(respostas_reais, previsoes_final)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xR6qgpIzln2p",
        "outputId": "0f161abb-e204-4906-8c81-384aa462ba3f"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 214, 2286],\n",
              "       [ 798, 1702]])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(cm, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "30vtswxHlpwA",
        "outputId": "2475c125-79bb-4f3d-e9a2-8e7e0628d772"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}