ML Elections Sentiment Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Brazil-Elections-Twitter-Sentiment\n",
    "\n",
    "# Twitter Sentiment Analysis using Sklearn (Under Review)\n",
    "\n",
    "# Description:\n",
    "#- Program that collects data from Twitter API and classify it into sentiment categories based on positive and negative Amazon reviews\n",
    "# Model Type \n",
    "### Sklearn - GridSearchCV, Decision Tree Classifier, Linear Regression, Naive Bayes, Logistic Regression, \n",
    "#20/10/2022\n",
    "\n",
    "# Version - V1.2\n",
    "\n",
    "# Goals:\n",
    "#- To build a ML model that is able to predict the sentiment from twitter hashtags, posts and profiles\n",
    "\n",
    "# Key Insights and Notes\n",
    "#- The model is analyzing a \"sentiment\", which is subjective\n",
    "#- Since this is an Election sentiment prediction, values can be affect everyday by new information\n",
    "#- We are translating the tweets from Portuguese to English, this can affect the model's accuracy\n",
    "#- Repeated tweets can be affecting the performance, you decide if you want to keep them  \n",
    " \n",
    "# Fixes\n",
    "#- Translation to Portuguese Added \n",
    "\n",
    "\n",
    "# Version Updates:\n",
    "#- API connections\n",
    "#- Classify by hashtags\n",
    "#- Sklearn implemented \n",
    "#- Amazon reviews database\n",
    "#- Save the model \n",
    "#- Statistics\n",
    "#- Added sentiment database\n",
    "#- Comparisons added \n",
    "#- Language translation added\n",
    "#- Remove duplicates \n",
    "\n",
    "\n",
    "# Future Implementations: \n",
    "#- Plot Analytical charts \n",
    "#- Add start_time and end_time parameters\n",
    "#- Create a list with market symbol and their sentiment\n",
    "#- Add time period analysis(select period option)\n",
    "#- Create sentiment dictionaries from web studies \n",
    "#- Hyperparameters Optimization \n",
    "#- Train and test with the Brazilian dataset\n",
    "#- Test with other ML models \n",
    "#- Test with ensemble model\n",
    "#- Analyse use of prepositions \n",
    "#- Create a Market Checker were it indicates the sentiment on the period and Prints the Chart Bullish or Bearish \n",
    "\n",
    "# Author - Luiz Gabriel Bongiolo\n",
    "\n",
    "# Credits & References \n",
    "#- https://developer.twitter.com/en/docs/tutorials/five-ways-to-convert-a-json-object-to-csv\n",
    "#- https://developer.twitter.com/en/docs/twitter-api/v1/tweets/search/api-reference/get-search-tweets\n",
    "#- Computer Science youtube channel - https://www.youtube.com/watch?v=ujId4ipkBio&t=280s\n",
    "#- Keith Galli - https://www.youtube.com/watch?v=M9Itm95JzL0\n",
    "#- http://jmcauley.ucsd.edu/data/amazon/\n",
    "#- Daniel Ellis Research - https://towardsdatascience.com/language-translation-using-python-bd8020772ccc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Libraries \n",
    "\n",
    "import tweepy\n",
    "from textblob import TextBlob\n",
    "from wordcloud import WordCloud\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import matplotlib.pyplot as plt \n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Class\n",
    "\n",
    "import random\n",
    "\n",
    "class Sentiment:\n",
    "    NEGATIVE = \"NEGATIVE\"\n",
    "    NEUTRAL = \"NEUTRAL\"\n",
    "    POSITIVE = \"POSITIVE\"\n",
    "\n",
    "class Review:\n",
    "    def __init__(self, text, score):\n",
    "        self.text = text\n",
    "        self.score = score\n",
    "        self.sentiment = self.get_sentiment()\n",
    "        \n",
    "    def get_sentiment(self):\n",
    "        if self.score <= 2:\n",
    "            return Sentiment.NEGATIVE\n",
    "        elif self.score == 3:\n",
    "            return Sentiment.NEUTRAL\n",
    "        else: #Score of 4 or 5\n",
    "            return Sentiment.POSITIVE\n",
    "\n",
    "class ReviewContainer:\n",
    "    def __init__(self, reviews):\n",
    "        self.reviews = reviews\n",
    "        \n",
    "    def get_text(self):\n",
    "        return [x.text for x in self.reviews]\n",
    "    \n",
    "    def get_sentiment(self):\n",
    "        return [x.sentiment for x in self.reviews]\n",
    "        \n",
    "    def evenly_distribute(self):\n",
    "        negative = list(filter(lambda x: x.sentiment == Sentiment.NEGATIVE, self.reviews))\n",
    "        positive = list(filter(lambda x: x.sentiment == Sentiment.POSITIVE, self.reviews))\n",
    "        positive_shrunk = positive[:len(negative)]\n",
    "        self.reviews = negative + positive_shrunk\n",
    "        random.shuffle(self.reviews)\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I hoped for Mia to have some peace in this book, but her story is so real and raw.  Broken World was so touching and emotional because you go from Mia\\'s trauma to her trying to cope.  I love the way the story displays how there is no \"just bouncing back\" from being sexually assaulted.  Mia showed us how those demons come for you every day and how sometimes they best you. I was so in the moment with Broken World and hurt with Mia because she was surrounded by people but so alone and I understood her feelings.  I found myself wishing I could give her some of my courage and strength or even just to be there for her.  Thank you Lizzy for putting a great character\\'s voice on a strong subject and making it so that other peoples story may be heard through Mia\\'s.'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "file_name = './Twitter Sentiment Analysis/ML Sentiment Analysis/data/sentiment/Books_small_10000.json'\n",
    "\n",
    "reviews = []\n",
    "with open(file_name) as f:\n",
    "    for line in f:\n",
    "        review = json.loads(line)\n",
    "        reviews.append(Review(review['reviewText'], review['overall']))\n",
    "        \n",
    "reviews[5].text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prepare the data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "training, test = train_test_split(reviews, test_size=0.33, random_state=42)\n",
    "\n",
    "train_container = ReviewContainer(training)\n",
    "\n",
    "test_container = ReviewContainer(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "436\n",
      "436\n"
     ]
    }
   ],
   "source": [
    "train_container.evenly_distribute()\n",
    "train_x = train_container.get_text()\n",
    "train_y = train_container.get_sentiment()\n",
    "\n",
    "test_container.evenly_distribute()\n",
    "test_x = test_container.get_text()\n",
    "test_y = test_container.get_sentiment()\n",
    "\n",
    "print(train_y.count(Sentiment.POSITIVE))\n",
    "print(train_y.count(Sentiment.NEGATIVE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This was an excellent book about multiple personality and what bullying can lead to!  The writer was talented and descriptive and kept the reader wanting to know what would happen next.  I would like to see more by this author.\n",
      "[[0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "#Words vectorization\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "\n",
    "# This book is great !\n",
    "# This book was so bad\n",
    "\n",
    "vectorizer = TfidfVectorizer()\n",
    "train_x_vectors = vectorizer.fit_transform(train_x)\n",
    "\n",
    "test_x_vectors = vectorizer.transform(test_x)\n",
    "\n",
    "print(train_x[0])\n",
    "print(train_x_vectors[0].toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NEGATIVE'], dtype='<U8')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prepare the ensanble model \n",
    "\n",
    "#Classification\n",
    "\n",
    "from sklearn import svm\n",
    "\n",
    "clf_svm = svm.SVC(kernel='linear')\n",
    "\n",
    "clf_svm.fit(train_x_vectors, train_y)\n",
    "\n",
    "test_x[0]\n",
    "\n",
    "clf_svm.predict(test_x_vectors[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NEGATIVE'], dtype='<U8')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "clf_dec = DecisionTreeClassifier()\n",
    "clf_dec.fit(train_x_vectors, train_y)\n",
    "\n",
    "clf_dec.predict(test_x_vectors[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NEGATIVE'], dtype='<U8')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Naive Bayes\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "clf_gnb = DecisionTreeClassifier()\n",
    "clf_gnb.fit(train_x_vectors, train_y)\n",
    "\n",
    "clf_gnb.predict(test_x_vectors[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NEGATIVE'], dtype='<U8')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Losgistic Regression\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf_log = LogisticRegression()\n",
    "clf_log.fit(train_x_vectors, train_y)\n",
    "\n",
    "clf_log.predict(test_x_vectors[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8076923076923077\n",
      "0.6586538461538461\n",
      "0.6322115384615384\n",
      "0.8052884615384616\n"
     ]
    }
   ],
   "source": [
    "# Mean Accuracy\n",
    "print(clf_svm.score(test_x_vectors, test_y))\n",
    "print(clf_dec.score(test_x_vectors, test_y))\n",
    "print(clf_gnb.score(test_x_vectors, test_y))\n",
    "print(clf_log.score(test_x_vectors, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.80582524, 0.80952381])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# F1 Scores\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1_score(test_y, clf_svm.predict(test_x_vectors), average=None, labels=[Sentiment.POSITIVE, Sentiment.NEGATIVE])\n",
    "#f1_score(test_y, clf_log.predict(test_x_vectors), average=None, labels=[Sentiment.POSITIVE, Sentiment.NEUTRAL, Sentiment.NEGATIVE])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['POSITIVE', 'NEGATIVE', 'NEGATIVE'], dtype='<U8')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set = ['very fun', \"bad book do not buy\", 'horrible waste of time']\n",
    "new_test = vectorizer.transform(test_set)\n",
    "\n",
    "clf_svm.predict(new_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "             param_grid={&#x27;C&#x27;: (1, 4, 8, 16, 32), &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: (1, 4, 8, 16, 32), &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={'C': (1, 4, 8, 16, 32), 'kernel': ('linear', 'rbf')})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Tuning the model with Grid Search\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "parameters = {'kernel': ('linear', 'rbf'), 'C': (1,4,8,16,32)}\n",
    "\n",
    "svc = svm.SVC()\n",
    "clf = GridSearchCV(svc, parameters, cv=5)\n",
    "clf.fit(train_x_vectors, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8100961538461539\n"
     ]
    }
   ],
   "source": [
    "print(clf.score(test_x_vectors, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save the model \n",
    "\n",
    "import pickle\n",
    "\n",
    "with open('./Twitter Sentiment Analysis/ML Sentiment Analysis/models/sentiment_classifier.pkl', 'wb') as f:\n",
    "    pickle.dump(clf, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the model \n",
    "\n",
    "with open('./Twitter Sentiment Analysis/ML Sentiment Analysis/models/sentiment_classifier.pkl', 'rb') as f:\n",
    "    loaded_clf = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It was a good book . The story and the characters came together nicely and the ending was awesome .\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['NEGATIVE'], dtype='<U8')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(test_x[0])\n",
    "\n",
    "loaded_clf.predict(test_x_vectors[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.linear_model import Perceptron\n",
    "\n",
    "#clf = Perceptron(tol=1e-3, random_state=0)\n",
    "#clf.fit(X, y)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the Twitter Key\n",
    "\n",
    "import os\n",
    "os.chdir(\"./Twitter Sentiment Analysis/\")\n",
    "\n",
    "log = pd.read_csv(\"Key.csv\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the twitter API credentials \n",
    "\n",
    "consumerKey = log[\"Key\"][0]\n",
    "consumerSecret = log[\"Key\"][1]\n",
    "accessToken = log[\"Key\"][2]\n",
    "accessTokenSecret = log[\"Key\"][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the authentication object \n",
    "\n",
    "authenticate = tweepy.OAuth1UserHandler(consumerKey, consumerSecret)\n",
    "\n",
    "#Set the access token and acces token secret\n",
    "\n",
    "authenticate.set_access_token(accessToken, accessTokenSecret)\n",
    "\n",
    "#Create the API object\n",
    "\n",
    "api = tweepy.API(authenticate, wait_on_rate_limit = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract 1000 tweets from hashtags\n",
    "\n",
    "keywords = \"#Bolsonaro\"\n",
    "keywords2 = \"#Lula\"\n",
    "limit=1000\n",
    "\n",
    "posts = tweepy.Cursor(api.search_tweets, q=keywords, count=100, tweet_mode=\"extended\").items(limit)\n",
    "posts2 = tweepy.Cursor(api.search_tweets, q=keywords2, count=100, tweet_mode=\"extended\").items(limit)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "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>Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@77_frota Fora Bozo e os filhos  do Bozo. E #L...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RT @zehdeabreu: MEU DEUS MINAS GERAIS!!!!!\\n❤️...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RT @willames_vieira: 🚨🚨 AGORA CAMINHADA LULA P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RT @gudryanneufert: Perguntei, agora pouco, ao...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RT @anjulitostes: Sem medo de ser feliz! Lula ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>RT @zehdeabreu: MEU DEUS MINAS GERAIS!!!!!\\n❤️...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>RT @wdiaspi: Maranhão e Piauí unidos por democ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>RT @wdiaspi: Maranhão e Piauí unidos por democ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>RT @zehdeabreu: MEU DEUS MINAS GERAIS!!!!!\\n❤️...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>RT @wdiaspi: Maranhão e Piauí unidos por democ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tweets\n",
       "0    @77_frota Fora Bozo e os filhos  do Bozo. E #L...\n",
       "1    RT @zehdeabreu: MEU DEUS MINAS GERAIS!!!!!\\n❤️...\n",
       "2    RT @willames_vieira: 🚨🚨 AGORA CAMINHADA LULA P...\n",
       "3    RT @gudryanneufert: Perguntei, agora pouco, ao...\n",
       "4    RT @anjulitostes: Sem medo de ser feliz! Lula ...\n",
       "..                                                 ...\n",
       "995  RT @zehdeabreu: MEU DEUS MINAS GERAIS!!!!!\\n❤️...\n",
       "996  RT @wdiaspi: Maranhão e Piauí unidos por democ...\n",
       "997  RT @wdiaspi: Maranhão e Piauí unidos por democ...\n",
       "998  RT @zehdeabreu: MEU DEUS MINAS GERAIS!!!!!\\n❤️...\n",
       "999  RT @wdiaspi: Maranhão e Piauí unidos por democ...\n",
       "\n",
       "[1000 rows x 1 columns]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a dataframe with a column called Tweets\n",
    "\n",
    "df = pd.DataFrame([tweet.full_text for tweet in posts], columns=[\"Tweets\"])\n",
    "\n",
    "df2 = pd.DataFrame([tweet.full_text for tweet in posts2], columns=[\"Tweets\"])\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>_frota Fora Bozo e os filhos  do Bozo. E Lula 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>: MEU DEUS MINAS GERAIS!!!!!\\n❤️❤️❤️❤️\\n\\nNing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>_vieira: 🚨🚨 AGORA CAMINHADA LULA PRESIDENTE 13...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>: Perguntei, agora pouco, ao candidato, Luis I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>: Sem medo de ser feliz! Lula lá!! ❤️\\n\\nLula ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>: MEU DEUS MINAS GERAIS!!!!!\\n❤️❤️❤️❤️\\n\\nNing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>: Maranhão e Piauí unidos por democracia, espe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>: Maranhão e Piauí unidos por democracia, espe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>: MEU DEUS MINAS GERAIS!!!!!\\n❤️❤️❤️❤️\\n\\nNing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>: Maranhão e Piauí unidos por democracia, espe...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tweets\n",
       "0     _frota Fora Bozo e os filhos  do Bozo. E Lula 13\n",
       "1    : MEU DEUS MINAS GERAIS!!!!!\\n❤️❤️❤️❤️\\n\\nNing...\n",
       "2    _vieira: 🚨🚨 AGORA CAMINHADA LULA PRESIDENTE 13...\n",
       "3    : Perguntei, agora pouco, ao candidato, Luis I...\n",
       "4    : Sem medo de ser feliz! Lula lá!! ❤️\\n\\nLula ...\n",
       "..                                                 ...\n",
       "995  : MEU DEUS MINAS GERAIS!!!!!\\n❤️❤️❤️❤️\\n\\nNing...\n",
       "996  : Maranhão e Piauí unidos por democracia, espe...\n",
       "997  : Maranhão e Piauí unidos por democracia, espe...\n",
       "998  : MEU DEUS MINAS GERAIS!!!!!\\n❤️❤️❤️❤️\\n\\nNing...\n",
       "999  : Maranhão e Piauí unidos por democracia, espe...\n",
       "\n",
       "[1000 rows x 1 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Clean the text \n",
    "#Create a function to clean the tweets \n",
    "\n",
    "def cleanTxt(text):\n",
    "    text = re.sub(r\"@[A-Za-z0-9]+\", \"\", text) #Removing @mentions \n",
    "    text = re.sub(r\"#\", \"\", text) #Removing #hashtags\n",
    "    text = re.sub(r\"RT[\\s]+\", \"\", text) #Removing RT\n",
    "    text = re.sub(r\"https?:\\/\\/\\S+\", \"\", text) #Removing hyperlink\n",
    "\n",
    "    return text \n",
    "\n",
    "df[\"Tweets\"] = df[\"Tweets\"].apply(cleanTxt)\n",
    "\n",
    "#Clean df2\n",
    "\n",
    "def cleanTxt(text):\n",
    "    text = re.sub(r\"@[A-Za-z0-9]+\", \"\", text) #Removing @mentions \n",
    "    text = re.sub(r\"#\", \"\", text) #Removing @mentions \n",
    "    text = re.sub(r\"RT[\\s]+\", \"\", text) #Removing RT\n",
    "    text = re.sub(r\"https?:\\/\\/\\S+\", \"\", text) #Removing hyperlink\n",
    "\n",
    "    return text \n",
    "\n",
    "df2[\"Tweets\"] = df2[\"Tweets\"].apply(cleanTxt)\n",
    "\n",
    "\n",
    "#Show the cleaned text \n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "#If you want to remove Duplicated tweets uncomment this \n",
    "\n",
    "#df = df.drop_duplicates()\n",
    "#df= df2.drop_duplicates()\n",
    "\n",
    "#df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Translating tweets to english :)\n",
    "\n",
    "import translators as ts\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "df['Tweets'] = df['Tweets'].apply(lambda x: ts.google(x, from_language='pt', to_language='en'))\n",
    "df2['Tweets'] = df2['Tweets'].apply(lambda x: ts.google(x, from_language='pt', to_language='en'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>: 🔮 Prediction 23: Brazilian election 🗳 \\n\\n W...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>: We will be today in Balneário Camboriú and C...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>_: Already! Gusttavo Lima and you on Superlive...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thanks Bolsonaro Bolsonaro reelected in 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>: I'm with Jair again! It's 2️⃣2️⃣! \\n Bolsona...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>_: I am not asking, but share this video with ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>And when will it be from Marcola and the CCP w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Lulanacadeia ptnuncais bolsonaroreeleitom2022 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>SuperLiveBolsonaro Superlivecombolsonaro Super...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>: Früher War Brasiliens Präsident Bolsonaro Fü...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tweets\n",
       "0    : 🔮 Prediction 23: Brazilian election 🗳 \\n\\n W...\n",
       "1    : We will be today in Balneário Camboriú and C...\n",
       "2    _: Already! Gusttavo Lima and you on Superlive...\n",
       "3         Thanks Bolsonaro Bolsonaro reelected in 2022\n",
       "4    : I'm with Jair again! It's 2️⃣2️⃣! \\n Bolsona...\n",
       "..                                                 ...\n",
       "995  _: I am not asking, but share this video with ...\n",
       "996  And when will it be from Marcola and the CCP w...\n",
       "997  Lulanacadeia ptnuncais bolsonaroreeleitom2022 ...\n",
       "998  SuperLiveBolsonaro Superlivecombolsonaro Super...\n",
       "999  : Früher War Brasiliens Präsident Bolsonaro Fü...\n",
       "\n",
       "[1000 rows x 1 columns]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweets</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>: 🔮 Prediction 23: Brazilian election 🗳 \\n\\n W...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>: We will be today in Balneário Camboriú and C...</td>\n",
       "      <td>POSITIVE</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>_: Already! Gusttavo Lima and you on Superlive...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thanks Bolsonaro Bolsonaro reelected in 2022</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>: I'm with Jair again! It's 2️⃣2️⃣! \\n Bolsona...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>_: I am not asking, but share this video with ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>And when will it be from Marcola and the CCP w...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Lulanacadeia ptnuncais bolsonaroreeleitom2022 ...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>SuperLiveBolsonaro Superlivecombolsonaro Super...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>: Früher War Brasiliens Präsident Bolsonaro Fü...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tweets Prediction\n",
       "0    : 🔮 Prediction 23: Brazilian election 🗳 \\n\\n W...   POSITIVE\n",
       "1    : We will be today in Balneário Camboriú and C...   POSITIVE\n",
       "2    _: Already! Gusttavo Lima and you on Superlive...   NEGATIVE\n",
       "3         Thanks Bolsonaro Bolsonaro reelected in 2022   POSITIVE\n",
       "4    : I'm with Jair again! It's 2️⃣2️⃣! \\n Bolsona...   NEGATIVE\n",
       "..                                                 ...        ...\n",
       "995  _: I am not asking, but share this video with ...   NEGATIVE\n",
       "996  And when will it be from Marcola and the CCP w...   POSITIVE\n",
       "997  Lulanacadeia ptnuncais bolsonaroreeleitom2022 ...   POSITIVE\n",
       "998  SuperLiveBolsonaro Superlivecombolsonaro Super...   POSITIVE\n",
       "999  : Früher War Brasiliens Präsident Bolsonaro Fü...   POSITIVE\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set2 = df[\"Tweets\"]\n",
    "new_test2 = vectorizer.transform(test_set2)\n",
    "\n",
    "clf_svm.predict(new_test2)\n",
    "\n",
    "\n",
    "\n",
    "prediction = pd.DataFrame(clf_svm.predict(new_test2), columns=[\"Prediction\"])\n",
    "\n",
    "\n",
    "df[\"Prediction\"] = prediction\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Tweets</th>\n",
       "      <th>Prediction2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>_Frota outside Bozo and the children of Bozo. ...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>: MY GOD MINAS GENERAL !!!!! \\n ❤️❤️❤️❤️ \\n\\n ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>_Vieira: 🚨🚨 Now walk Lula President 13 \\n\\n Ar...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>: I asked, now little, to the candidate, Luis ...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>: Without fear of being happy! Lula there !! ❤...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>: MY GOD MINAS GENERAL !!!!! \\n ❤️❤️❤️❤️ \\n\\n ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>: Maranhão and Piauí united for democracy, hop...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>: Maranhão and Piauí united for democracy, hop...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>: MY GOD MINAS GENERAL !!!!! \\n ❤️❤️❤️❤️ \\n\\n ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>: Maranhão and Piauí united for democracy, hop...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tweets Prediction2\n",
       "0    _Frota outside Bozo and the children of Bozo. ...    POSITIVE\n",
       "1    : MY GOD MINAS GENERAL !!!!! \\n ❤️❤️❤️❤️ \\n\\n ...    NEGATIVE\n",
       "2    _Vieira: 🚨🚨 Now walk Lula President 13 \\n\\n Ar...    POSITIVE\n",
       "3    : I asked, now little, to the candidate, Luis ...    POSITIVE\n",
       "4    : Without fear of being happy! Lula there !! ❤...    POSITIVE\n",
       "..                                                 ...         ...\n",
       "995  : MY GOD MINAS GENERAL !!!!! \\n ❤️❤️❤️❤️ \\n\\n ...    NEGATIVE\n",
       "996  : Maranhão and Piauí united for democracy, hop...    POSITIVE\n",
       "997  : Maranhão and Piauí united for democracy, hop...    POSITIVE\n",
       "998  : MY GOD MINAS GENERAL !!!!! \\n ❤️❤️❤️❤️ \\n\\n ...    NEGATIVE\n",
       "999  : Maranhão and Piauí united for democracy, hop...    POSITIVE\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Text second #\n",
    "\n",
    "test_set3 = df2[\"Tweets\"]\n",
    "new_test3 = vectorizer.transform(test_set3)\n",
    "\n",
    "clf_svm.predict(new_test3)\n",
    "\n",
    "\n",
    "\n",
    "prediction2 = pd.DataFrame(clf_svm.predict(new_test3), columns=[\"Prediction2\"])\n",
    "\n",
    "\n",
    "df2[\"Prediction2\"] = prediction2\n",
    "\n",
    "df2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check the distribution of the polarity with Word Cloud\n",
    "\n",
    "allWords = \" \".join( [twts for twts in df[\"Tweets\"]])\n",
    "wordCloud = WordCloud(width = 500, height=300, random_state = 42, max_font_size = 119).generate(allWords)\n",
    "\n",
    "plt.imshow(wordCloud, interpolation = \"bilinear\")\n",
    "\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check the distribution of the polarity with Word Cloud\n",
    "\n",
    "allWords2 = \" \".join( [twts for twts in df2[\"Tweets\"]])\n",
    "wordCloud2 = WordCloud(width = 500, height=300, random_state = 42, max_font_size = 119).generate(allWords2)\n",
    "\n",
    "plt.imshow(wordCloud2, interpolation = \"bilinear\")\n",
    "\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "POSITIVE    619\n",
       "NEGATIVE    381\n",
       "Name: Prediction, dtype: int64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count values df1\n",
    "\n",
    "df[\"Prediction\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         1000\n",
       "unique           2\n",
       "top       POSITIVE\n",
       "freq           619\n",
       "Name: Prediction, dtype: object"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Describe df1\n",
    "\n",
    "df[\"Prediction\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "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>Tweets</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>: 🔮 Prediction 23: Brazilian election 🗳 \\n\\n W...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>: We will be today in Balneário Camboriú and C...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thanks Bolsonaro Bolsonaro reelected in 2022</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>_: We need you, Patriot! 🇧🇷 \\n\\n Be a campaign...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Calid path is one that will lead the PT to pow...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>: URGENT: Bolsonaro turned! \\n\\n According to ...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>And when will it be from Marcola and the CCP w...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Lulanacadeia ptnuncais bolsonaroreeleitom2022 ...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>SuperLiveBolsonaro Superlivecombolsonaro Super...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>: Früher War Brasiliens Präsident Bolsonaro Fü...</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>619 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Tweets Prediction\n",
       "0    : 🔮 Prediction 23: Brazilian election 🗳 \\n\\n W...   POSITIVE\n",
       "1    : We will be today in Balneário Camboriú and C...   POSITIVE\n",
       "3         Thanks Bolsonaro Bolsonaro reelected in 2022   POSITIVE\n",
       "5    _: We need you, Patriot! 🇧🇷 \\n\\n Be a campaign...   POSITIVE\n",
       "6    Calid path is one that will lead the PT to pow...   POSITIVE\n",
       "..                                                 ...        ...\n",
       "994  : URGENT: Bolsonaro turned! \\n\\n According to ...   POSITIVE\n",
       "996  And when will it be from Marcola and the CCP w...   POSITIVE\n",
       "997  Lulanacadeia ptnuncais bolsonaroreeleitom2022 ...   POSITIVE\n",
       "998  SuperLiveBolsonaro Superlivecombolsonaro Super...   POSITIVE\n",
       "999  : Früher War Brasiliens Präsident Bolsonaro Fü...   POSITIVE\n",
       "\n",
       "[619 rows x 2 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show only positive from df1\n",
    "\n",
    "df[df[\"Prediction\"]==\"POSITIVE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#So lets only with the ML model \n",
    "\n",
    "#Show the value counts \n",
    "\n",
    "df[\"Prediction\"].value_counts()\n",
    "\n",
    "#plot\n",
    "\n",
    "plt.title(\"Sentiment Prediction \" + str(keywords))\n",
    "plt.xlabel(\"Sentiment\")\n",
    "plt.ylabel(\"Counts\")\n",
    "df[\"Prediction\"].value_counts().plot(kind=\"bar\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Show the value counts \n",
    "\n",
    "df2[\"Prediction2\"].value_counts()\n",
    "\n",
    "#plot\n",
    "\n",
    "plt.title(\"Sentiment Prediction \" + str(keywords2))\n",
    "plt.xlabel(\"Sentiment\")\n",
    "plt.ylabel(\"Counts\")\n",
    "df2[\"Prediction2\"].value_counts().plot(kind=\"bar\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Bolsonaro\n",
      "Positive 61.9%\n",
      "Negative 38.1%\n",
      "####################\n",
      "#Lula\n",
      "Positive 75.9%\n",
      "Negative 24.099999999999998%\n",
      "####################\n"
     ]
    }
   ],
   "source": [
    "#Percentage of Positive tweets \n",
    "\n",
    "ptweets = df[df.Prediction == \"POSITIVE\"] \n",
    "ptweets = ptweets[\"Tweets\"]\n",
    "ptweets = (len(ptweets) / len(df)) * 100\n",
    "\n",
    "ntweets = df[df.Prediction == \"NEGATIVE\"] \n",
    "ntweets = ntweets[\"Tweets\"]\n",
    "ntweets = (len(ntweets) / len(df)) * 100\n",
    "\n",
    "ptweets2 = df2[df2.Prediction2 == \"POSITIVE\"] \n",
    "ptweets2 = ptweets2[\"Tweets\"]\n",
    "ptweets2 = (len(ptweets2) / len(df2)) * 100\n",
    "\n",
    "ntweets2 = df2[df2.Prediction2 == \"NEGATIVE\"] \n",
    "ntweets2 = ntweets2[\"Tweets\"]\n",
    "ntweets2 = (len(ntweets2) / len(df2)) * 100\n",
    "\n",
    "\n",
    "\n",
    "print(str(keywords))\n",
    "print(\"Positive \" + str(ptweets)+ \"%\")\n",
    "print(\"Negative \"+ str(ntweets) + \"%\")\n",
    "print(\"####################\")\n",
    "print(str(keywords2))\n",
    "print(\"Positive \" + str(ptweets2)+ \"%\")\n",
    "print(\"Negative \"+ str(ntweets2) + \"%\")\n",
    "print(\"####################\")"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
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 },
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