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ronaldalbrt/AnaliseDeTweetsComPython.ipynb

Created November 16, 2021 15:37
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Notebook minicurso de Análise de Tweets com Python
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AnaliseDeTweetsComPython.ipynb
{
"nbformat": 4,
"nbformat_minor": 5,
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
},
"colab": {
"name": "AnaliseDeTweetsComPython.ipynb",
"provenance": [],
"collapsed_sections": []
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "8d95e20a"
},
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import nltk\n",
"from nltk.tokenize import TweetTokenizer\n",
"import spacy\n",
"import regex as re\n",
"import unicodedata\n",
"from bs4 import BeautifulSoup\n",
"from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.base import clone"
],
"id": "8d95e20a",
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "7ace75e1"
},
"source": [
"# Carregando os dados"
],
"id": "7ace75e1"
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "ff67a636",
"outputId": "5e211f1e-4c9e-4828-8756-eab697440698"
},
"source": [
"hs_df = pd.read_csv('https://raw.githubusercontent.com/UFRJ-Analytica/raspagem-de-dados-publicos/main/analise_de_tweets_com_python/hs.csv')\n",
"hs_df.head()"
],
"id": "ff67a636",
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>text</th>\n",
" <th>homophobia</th>\n",
" <th>obscene</th>\n",
" <th>insult</th>\n",
" <th>racism</th>\n",
" <th>misogyny</th>\n",
" <th>xenophobia</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Meu nivel de amizade com isis é ela ter meu in...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>rt @user @user o cara adultera dados, que fora...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>@user @user @user o cara só é simplesmente o m...</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>eu to chorando vei vsf e eu nem staneio izone ...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Eleitor do Bolsonaro é tão ignorante q não per...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text ... xenophobia\n",
"0 Meu nivel de amizade com isis é ela ter meu in... ... 0.0\n",
"1 rt @user @user o cara adultera dados, que fora... ... 0.0\n",
"2 @user @user @user o cara só é simplesmente o m... ... 0.0\n",
"3 eu to chorando vei vsf e eu nem staneio izone ... ... 0.0\n",
"4 Eleitor do Bolsonaro é tão ignorante q não per... ... 0.0\n",
"\n",
"[5 rows x 7 columns]"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "a41b95fc"
},
"source": [
"aux = hs_df[['homophobia','obscene', 'insult', 'racism', 'misogyny','xenophobia']].apply(lambda x: np.where(x > 0, 1, x) , axis=1, result_type='expand')\n",
"\n",
"hs_df[['homophobia','obscene', 'insult', 'racism', 'misogyny','xenophobia']] = aux"
],
"id": "a41b95fc",
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 350
},
"id": "d96af958",
"outputId": "53ed4792-6c02-440c-fb8e-44c906727318"
},
"source": [
"tweet_sum_df = hs_df[['homophobia','obscene', 'insult', 'racism', 'misogyny','xenophobia']].sum()\n",
"\n",
"fig = plt.figure(figsize = (10, 5))\n",
"\n",
"plt.bar(x=tweet_sum_df.index, height=tweet_sum_df.values, width = 0.4)\n",
" \n",
"plt.xlabel(\"Classes\")\n",
"plt.ylabel(\"No. de Tweets classificados\")\n",
"plt.title(\"Classificação de Tweets\")\n",
"plt.show()"
],
"id": "d96af958",
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "c0f86524",
"outputId": "e3213cd1-7e03-4ab9-a197-439f0f535252"
},
"source": [
"pol_tweets = pd.read_csv('https://raw.githubusercontent.com/UFRJ-Analytica/raspagem-de-dados-publicos/main/analise_de_tweets_com_python/tweets.csv').drop('Unnamed: 0', axis=1)\n",
"pol_tweets"
],
"id": "c0f86524",
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>created_at</th>\n",
" <th>tweet</th>\n",
" <th>author</th>\n",
" <th>retweet</th>\n",
" <th>in_reply_to</th>\n",
" <th>coordinates</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-11-11 22:21:52+00:00</td>\n",
" <td>RT @OGloboPolitica: Sonar: Novo vídeo do #Bols...</td>\n",
" <td>MarceloFreixo</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2021-11-11 22:14:55+00:00</td>\n",
" <td>Gilberto Gil é imortal há muito tempo.</td>\n",
" <td>MarceloFreixo</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2021-11-11 21:42:39+00:00</td>\n",
" <td>RT @arcelinobsilva: Você sabe o que é #Bolsoca...</td>\n",
" <td>MarceloFreixo</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2021-11-11 21:28:39+00:00</td>\n",
" <td>@arcelinobsilva @felipeneto @alessandromolon @...</td>\n",
" <td>MarceloFreixo</td>\n",
" <td>False</td>\n",
" <td>arcelinobsilva</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2021-11-11 21:28:26+00:00</td>\n",
" <td>RT @arcelinobsilva: Você já assistiu o vídeo q...</td>\n",
" <td>MarceloFreixo</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26995</th>\n",
" <td>2021-04-18 00:50:04+00:00</td>\n",
" <td>Agradeço a todos que participaram do debate na...</td>\n",
" <td>cirogomes</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26996</th>\n",
" <td>2021-04-18 00:45:54+00:00</td>\n",
" <td>Esse projeto deve ter um prazo objetivamente a...</td>\n",
" <td>cirogomes</td>\n",
" <td>False</td>\n",
" <td>cirogomes</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26997</th>\n",
" <td>2021-04-18 00:45:54+00:00</td>\n",
" <td>O Brasil não vai sair dessa tragédia com mais ...</td>\n",
" <td>cirogomes</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26998</th>\n",
" <td>2021-04-18 00:06:13+00:00</td>\n",
" <td>A política exterior brasileira precisa buscar ...</td>\n",
" <td>cirogomes</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26999</th>\n",
" <td>2021-04-18 00:02:00+00:00</td>\n",
" <td>Repare: 86% da química fina brasileira é impor...</td>\n",
" <td>cirogomes</td>\n",
" <td>False</td>\n",
" <td>cirogomes</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>27000 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" created_at ... coordinates\n",
"0 2021-11-11 22:21:52+00:00 ... NaN\n",
"1 2021-11-11 22:14:55+00:00 ... NaN\n",
"2 2021-11-11 21:42:39+00:00 ... NaN\n",
"3 2021-11-11 21:28:39+00:00 ... NaN\n",
"4 2021-11-11 21:28:26+00:00 ... NaN\n",
"... ... ... ...\n",
"26995 2021-04-18 00:50:04+00:00 ... NaN\n",
"26996 2021-04-18 00:45:54+00:00 ... NaN\n",
"26997 2021-04-18 00:45:54+00:00 ... NaN\n",
"26998 2021-04-18 00:06:13+00:00 ... NaN\n",
"26999 2021-04-18 00:02:00+00:00 ... NaN\n",
"\n",
"[27000 rows x 6 columns]"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "666738f1",
"outputId": "49cd3c0d-f74a-40fd-afa9-7334039011db"
},
"source": [
"pol_tweets.author.unique()"
],
"id": "666738f1",
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['MarceloFreixo', 'jairbolsonaro', 'eduardopaes', 'CarlosBolsonaro',\n",
" 'BolsonaroSP', 'FlavioBolsonaro', 'CarlaZambelli38', 'Biakicis',\n",
" 'MottaTarcisio', 'LulaOficial', 'GuilhermeBoulos', 'gleisi',\n",
" 'mariadorosario', 'jandira_feghali', 'ManuelaDavila',\n",
" 'Haddad_Fernando', 'MarinaSilva', 'cirogomes'], dtype=object)"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "e4fba21b"
},
"source": [
"# Tratamento do texto, Lemmatização e Tokenização"
],
"id": "e4fba21b"
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "14221f2c",
"outputId": "ab74f967-0962-4ef9-8e3d-95a5624b2b07"
},
"source": [
"!python -m spacy download pt_core_news_sm\n",
"!python -m spacy link pt_core_news_sm pt --force\n",
"nlp = spacy.load('pt')\n",
"tokenizer = TweetTokenizer()\n",
"\n",
"nltk.download('stopwords')\n",
"stopword_list = nltk.corpus.stopwords.words('portuguese')\n",
"stopword_list[:10]"
],
"id": "14221f2c",
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting pt_core_news_sm==2.2.5\n",
" Downloading https://github.com/explosion/spacy-models/releases/download/pt_core_news_sm-2.2.5/pt_core_news_sm-2.2.5.tar.gz (21.2 MB)\n",
"\u001b[K |████████████████████████████████| 21.2 MB 5.2 MB/s \n",
"\u001b[?25hRequirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.7/dist-packages (from pt_core_news_sm==2.2.5) (2.2.4)\n",
"Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->pt_core_news_sm==2.2.5) (1.0.0)\n",
"Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->pt_core_news_sm==2.2.5) (1.0.6)\n",
"Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->pt_core_news_sm==2.2.5) (0.8.2)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->pt_core_news_sm==2.2.5) (57.4.0)\n",
"Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->pt_core_news_sm==2.2.5) (7.4.0)\n",
"Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->pt_core_news_sm==2.2.5) (0.4.1)\n",
"Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->pt_core_news_sm==2.2.5) (1.19.5)\n",
"Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->pt_core_news_sm==2.2.5) (2.23.0)\n",
"Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->pt_core_news_sm==2.2.5) (3.0.6)\n",
"Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->pt_core_news_sm==2.2.5) (1.1.3)\n",
"Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->pt_core_news_sm==2.2.5) (4.62.3)\n",
"Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->pt_core_news_sm==2.2.5) (2.0.6)\n",
"Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->pt_core_news_sm==2.2.5) (1.0.5)\n",
"Requirement already satisfied: importlib-metadata>=0.20 in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->pt_core_news_sm==2.2.5) (4.8.2)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->pt_core_news_sm==2.2.5) (3.6.0)\n",
"Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->pt_core_news_sm==2.2.5) (3.10.0.2)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->pt_core_news_sm==2.2.5) (2021.10.8)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->pt_core_news_sm==2.2.5) (2.10)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->pt_core_news_sm==2.2.5) (3.0.4)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->pt_core_news_sm==2.2.5) (1.24.3)\n",
"\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
"You can now load the model via spacy.load('pt_core_news_sm')\n",
"\u001b[38;5;2m✔ Linking successful\u001b[0m\n",
"/usr/local/lib/python3.7/dist-packages/pt_core_news_sm -->\n",
"/usr/local/lib/python3.7/dist-packages/spacy/data/pt\n",
"You can now load the model via spacy.load('pt')\n",
"[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['de', 'a', 'o', 'que', 'e', 'é', 'do', 'da', 'em', 'um']"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "08673fb0",
"outputId": "09551910-ab78-425b-cb7f-9a862a226039"
},
"source": [
"tokenizer.tokenize(hs_df.text[20999])"
],
"id": "08673fb0",
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['saudades',\n",
" 'da',\n",
" 'iasmin',\n",
" 'de',\n",
" '2017',\n",
" 'eu',\n",
" 'era',\n",
" 'gótica',\n",
" 'demais',\n",
" 'pqp']"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "a397d8a8"
},
"source": [
"'''Remove stopwords'''\n",
"def remover_stop_words(text):\n",
" tokens = tokenizer.tokenize(text)\n",
" tokens = [token.strip() for token in tokens]\n",
" filtered_tokens = [token for token in tokens if token.lower() not in stopword_list]\n",
" filtered_tokens = [token for token in tokens if token.lower() not in stopword_list]\n",
" return filtered_tokens\n",
"\n",
"'''Remove acentos'''\n",
"def remover_acentos(text):\n",
" text = unicodedata.normalize('NFKD', text).encode('ascii', 'ignore').decode('utf-8', 'ignore')\n",
" return text\n",
"\n",
"'''Lematização\n",
"O objetivo da lematização é reduzir (deflexionar) uma palavra a sua base.\n",
"\n",
"A lematização reduz a base utilizando um vocabulário e a análise morfológica das palavras.\n",
"\n",
"Exemplos:\n",
"1. A palavra \"walk\" é a base para \"walking\", e é corretamente reduzida pelo stemming e pela lematização.\n",
"2. A palavra \"better\" tem \"good\" como base (ou lema). Esse link é perdido pelo stemming.\n",
"3. A palavra \"meeting\" pode ser reduzido para um substantivo ou um verbo dependendo do contexto. \n",
" E.g., \"in our last meeting\" ou \"We are meeting again tomorrow\". \n",
" A lematização consegue fazer a redução de forma correta.\n",
"\n",
"Deve-se escolher um ou outro. A desvantagem da lematização é que ela é mais custosa do ponto de vista computacional.\n",
"'''\n",
"def lemmatize(text):\n",
" text = nlp(text)\n",
" text = ' '.join([word.lemma_ if word.lemma_ != '-PRON-' else word.text for word in text])\n",
" return text\n",
"\n",
"'''Remove links'''\n",
"def strip_html_tags(text):\n",
" text = re.sub(r\"http[s]?://\\S+\", \"\", text)\n",
" text = re.sub(r\"\\s+\", \" \", text)\n",
" soup = BeautifulSoup(text, \"html.parser\")\n",
" stripped_text = soup.get_text()\n",
" return stripped_text"
],
"id": "a397d8a8",
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9ede7b97",
"outputId": "4aa6b224-18a9-4d3b-f306-6e37892d5eb1"
},
"source": [
"#Funções para limpar texto: chama todas as funções declaradas acima e faz mais alguns pré processamentos\n",
"def clean(doc):\n",
" # remove links\n",
" doc = strip_html_tags(doc)\n",
"\n",
" # coloca tudo em caixa baixa\n",
" doc = doc.lower()\n",
"\n",
" # remove linhas em branco\n",
" doc = re.sub(r'[\\r|\\n|\\r\\n|\\s]+', ' ', doc)\n",
"\n",
" # lemmatização\n",
" doc = lemmatize(doc)\n",
" \n",
" # remove acentuação\n",
" doc = remover_acentos(doc)\n",
"\n",
" # remove stopwords\n",
" doc = remover_stop_words(doc)\n",
" \n",
" return doc # retorna corpus pré processado\n",
"\n",
"clean(hs_df.text[20999])"
],
"id": "9ede7b97",
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['saudade', 'iasmin', '2017', 'ser', 'gotico', 'demais', 'pqp']"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8e6cb04d"
},
"source": [
"# Feature Extraction"
],
"id": "8e6cb04d"
},
{
"cell_type": "markdown",
"metadata": {
"id": "680fe2f0"
},
"source": [
"## Bag of Words"
],
"id": "680fe2f0"
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9876facd",
"outputId": "b55161c1-f591-44af-9f1d-1add9731a44d"
},
"source": [
"bow = CountVectorizer(tokenizer=clean) \n",
"bow_fit = bow.fit_transform(hs_df.text)\n",
"bow.get_feature_names_out()"
],
"id": "9876facd",
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['!', '\"', '#', ..., 'zzeeeeus', 'zzz', '~'], dtype=object)"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 600
},
"id": "c8971f8e",
"outputId": "5d27d9d4-c822-4dc7-d5a8-24eedce9a0ae"
},
"source": [
"bow_df = pd.DataFrame(bow_fit.toarray(), columns=bow.get_feature_names_out())\n",
"bow_df"
],
"id": "c8971f8e",
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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" <th>#belaafeia</th>\n",
" <th>#esquemao</th>\n",
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" <th>#mylove</th>\n",
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" <th>.. ..</th>\n",
" <th>...</th>\n",
" <th>... .</th>\n",
" <th>... ...</th>\n",
" <th>/</th>\n",
" <th>...</th>\n",
" <th>zi</th>\n",
" <th>zica</th>\n",
" <th>zico</th>\n",
" <th>zicou</th>\n",
" <th>zidane</th>\n",
" <th>ziel</th>\n",
" <th>zika</th>\n",
" <th>zimbabue</th>\n",
" <th>zimbrar</th>\n",
" <th>ziplox</th>\n",
" <th>zjzkzkskskkzkjjxjs</th>\n",
" <th>zn</th>\n",
" <th>zoandoh</th>\n",
" <th>zoar</th>\n",
" <th>zodiaco</th>\n",
" <th>zoeira</th>\n",
" <th>zoeirinhas</th>\n",
" <th>zoi</th>\n",
" <th>zoia</th>\n",
" <th>zoiao</th>\n",
" <th>zoiuda</th>\n",
" <th>zombar</th>\n",
" <th>zombie</th>\n",
" <th>zonar</th>\n",
" <th>zoologico</th>\n",
" <th>zoom</th>\n",
" <th>zorra</th>\n",
" <th>zoto</th>\n",
" <th>zovo</th>\n",
" <th>zrg</th>\n",
" <th>zuando</th>\n",
" <th>zuar</th>\n",
" <th>zuavam</th>\n",
" <th>zulema</th>\n",
" <th>zulive</th>\n",
" <th>zumbir</th>\n",
" <th>zunir</th>\n",
" <th>zzeeeeus</th>\n",
" <th>zzz</th>\n",
" <th>~</th>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20995</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20996</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20997</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" ! \" # #avantipalestra #belaafeia ... zumbir zunir zzeeeeus zzz ~\n",
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},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6936c190"
},
"source": [
"## tf-idf: Term Frequency - Inverse Document Frequency"
],
"id": "6936c190"
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "371e9734",
"outputId": "c42a1919-e792-48be-cd38-ecaa6626e28e"
},
"source": [
"tfidf = TfidfVectorizer(tokenizer=clean) \n",
"tfidf_fit = tfidf.fit_transform(hs_df.text)\n",
"tfidf.get_feature_names_out()"
],
"id": "371e9734",
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['!', '\"', '#', ..., 'zzeeeeus', 'zzz', '~'], dtype=object)"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 600
},
"id": "56890f54",
"outputId": "d9cabaf8-97cc-47fc-f429-2fd26968361a"
},
"source": [
"tfidf_df = pd.DataFrame(tfidf_fit.toarray(), columns=tfidf.get_feature_names_out())\n",
"tfidf_df"
],
"id": "56890f54",
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
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" </tr>\n",
" <tr>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
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" <tr>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>21000 rows × 18838 columns</p>\n",
"</div>"
],
"text/plain": [
" ! \" # #avantipalestra ... zunir zzeeeeus zzz ~\n",
"0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0\n",
"1 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0\n",
"2 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0\n",
"3 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0\n",
"4 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0\n",
"... ... ... ... ... ... ... ... ... ...\n",
"20995 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0\n",
"20996 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0\n",
"20997 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0\n",
"20998 0.0 0.0 0.532503 0.0 ... 0.0 0.0 0.0 0.0\n",
"20999 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0\n",
"\n",
"[21000 rows x 18838 columns]"
]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6c932a3a"
},
"source": [
"# Aprendizado de Máquina"
],
"id": "6c932a3a"
},
{
"cell_type": "code",
"metadata": {
"id": "ab20fadd"
},
"source": [
"X = tfidf_df.values\n",
"y = hs_df[['homophobia', 'obscene', 'insult', 'racism', 'misogyny', 'xenophobia']]"
],
"id": "ab20fadd",
"execution_count": 16,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "34007035"
},
"source": [
"## Árvores de Decisão"
],
"id": "34007035"
},
{
"cell_type": "code",
"metadata": {
"id": "c0531b68",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "fb94af90-6eac-45ce-dae0-028804dd40d8"
},
"source": [
"clf_tree = DecisionTreeClassifier(criterion=\"entropy\")\n",
"\n",
"scores_tree = cross_val_score(clf_tree, X, y['homophobia'], cv=5)\n",
"scores_tree"
],
"id": "c0531b68",
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0.98666667, 0.98595238, 0.98714286, 0.98047619, 0.98404762])"
]
},
"metadata": {},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "f596e956"
},
"source": [
"clf_tree_dict = {}\n",
"\n",
"for c in y.columns:\n",
" clf_tree_dict[c] = clone(clf_tree).fit(X, y[c])"
],
"id": "f596e956",
"execution_count": 17,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "07ee7072",
"outputId": "c8d7a0bc-afb5-405a-9f23-bfa44568f296"
},
"source": [
"fig = plt.figure(figsize=(25,20))\n",
"_ = plot_tree(clf_tree_dict['homophobia'], feature_names=tfidf_df.columns, class_names=list(str(y.homophobia.unique())), filled=True)"
],
"id": "07ee7072",
"execution_count": 18,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1800x1440 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lT77mGLLgLna"
},
"source": [
"## Florestas Aleatórias"
],
"id": "lT77mGLLgLna"
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6Z1-TeHWgQEa",
"outputId": "453fac5b-529b-4bd8-d82c-af65c64a4b5c"
},
"source": [
"clf_random_forest = RandomForestClassifier(n_estimators=100, bootstrap=True, max_features=\"sqrt\", criterion=\"entropy\")\n",
"\n",
"scores_random_forest = cross_val_score(clf_random_forest, X, y['homophobia'], cv=5)\n",
"scores_random_forest"
],
"id": "6Z1-TeHWgQEa",
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0.9852381 , 0.98428571, 0.98452381, 0.9847619 , 0.9847619 ])"
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "TOXEwk_1gfKh"
},
"source": [
"clf_forest_dict = {}\n",
"\n",
"for c in y.columns:\n",
" clf_forest_dict[c] = clone(clf_random_forest).fit(X, y[c])"
],
"id": "TOXEwk_1gfKh",
"execution_count": 18,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ah86n_rxjHfr"
},
"source": [
"## Regressão Logística"
],
"id": "Ah86n_rxjHfr"
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ilZzOD52izSg",
"outputId": "d027aac8-e9dd-490f-921a-2bd95d160282"
},
"source": [
"clf_log = LogisticRegression(random_state=43, max_iter=1000)\n",
"\n",
"scores_logistic = cross_val_score(clf_log, X, y['homophobia'], cv=5)\n",
"scores_logistic"
],
"id": "ilZzOD52izSg",
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0.98452381, 0.98452381, 0.9852381 , 0.98452381, 0.98380952])"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "rBnPrw2-i12i"
},
"source": [
"clf_log_dict = {}\n",
"\n",
"for c in y.columns:\n",
" clf_log_dict[c] = clone(clf_log).fit(X, y[c])"
],
"id": "rBnPrw2-i12i",
"execution_count": 20,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "M_Z2iGpOjegQ"
},
"source": [
"# Realizando as predições"
],
"id": "M_Z2iGpOjegQ"
},
{
"cell_type": "code",
"metadata": {
"id": "eVpLEyLo8XrD"
},
"source": [
"X_to_predict = tfidf.transform(pol_tweets.tweet)"
],
"id": "eVpLEyLo8XrD",
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "4Z7cLhPMjgo3"
},
"source": [
"for c in y.columns:\n",
" pol_tweets[c] = clf_log_dict[c].predict(X_to_predict)"
],
"id": "4Z7cLhPMjgo3",
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2Zyean52QYpV",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9707e6ba-f965-4c79-b1f0-308b2b23c4a7"
},
"source": [
"for c in y.columns:\n",
" print(c+\": \"+str(np.sum(pol_tweets[c])))"
],
"id": "2Zyean52QYpV",
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"homophobia: 0.0\n",
"obscene: 13.0\n",
"insult: 213.0\n",
"racism: 0.0\n",
"misogyny: 0.0\n",
"xenophobia: 0.0\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 350
},
"id": "S6a3kPHxH3Gn",
"outputId": "0860734a-435e-47a8-a01e-fc87059c745c"
},
"source": [
"obscene_sum = pol_tweets.groupby(by=[\"author\"]).sum()['obscene']\n",
"obscene_sum = obscene_sum[obscene_sum > 0]\n",
"\n",
"fig = plt.figure(figsize = (10, 5))\n",
"\n",
"plt.bar(x=obscene_sum.index, height=obscene_sum.values, width = 0.4)\n",
" \n",
"plt.xlabel(\"Políticos\")\n",
"plt.ylabel(\"No. de Tweets classificados como obscenos\")\n",
"plt.title(\"Classificação de Tweets\")\n",
"plt.show()"
],
"id": "S6a3kPHxH3Gn",
"execution_count": 33,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 290
},
"id": "ALym51uRIrfr",
"outputId": "2e29e9d6-bf65-4a8c-b40c-af4a14e6334d"
},
"source": [
"insult_sum = pol_tweets.groupby(by=[\"author\"]).sum()['insult']\n",
"insult_sum = insult_sum[insult_sum > 0]\n",
"\n",
"fig = plt.figure(figsize = (30, 5))\n",
"\n",
"plt.bar(x=insult_sum.index, height=insult_sum.values, width = 0.5)\n",
" \n",
"plt.xlabel(\"Políticos\")\n",
"plt.ylabel(\"No. de Tweets classificados como insultos\")\n",
"plt.title(\"Classificação de Tweets\")\n",
"plt.show()"
],
"id": "ALym51uRIrfr",
"execution_count": 39,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 2160x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
}
]
}
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