Tratamento.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import re\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Leitura dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>link</th>\n",
       "      <th>headline</th>\n",
       "      <th>category</th>\n",
       "      <th>short_description</th>\n",
       "      <th>authors</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://www.huffpost.com/entry/covid-boosters-...</td>\n",
       "      <td>Over 4 Million Americans Roll Up Sleeves For O...</td>\n",
       "      <td>U.S. NEWS</td>\n",
       "      <td>Health experts said it is too early to predict...</td>\n",
       "      <td>Carla K. Johnson, AP</td>\n",
       "      <td>2022-09-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://www.huffpost.com/entry/american-airlin...</td>\n",
       "      <td>American Airlines Flyer Charged, Banned For Li...</td>\n",
       "      <td>U.S. NEWS</td>\n",
       "      <td>He was subdued by passengers and crew when he ...</td>\n",
       "      <td>Mary Papenfuss</td>\n",
       "      <td>2022-09-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://www.huffpost.com/entry/funniest-tweets...</td>\n",
       "      <td>23 Of The Funniest Tweets About Cats And Dogs ...</td>\n",
       "      <td>COMEDY</td>\n",
       "      <td>\"Until you have a dog you don't understand wha...</td>\n",
       "      <td>Elyse Wanshel</td>\n",
       "      <td>2022-09-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://www.huffpost.com/entry/funniest-parent...</td>\n",
       "      <td>The Funniest Tweets From Parents This Week (Se...</td>\n",
       "      <td>PARENTING</td>\n",
       "      <td>\"Accidentally put grown-up toothpaste on my to...</td>\n",
       "      <td>Caroline Bologna</td>\n",
       "      <td>2022-09-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://www.huffpost.com/entry/amy-cooper-lose...</td>\n",
       "      <td>Woman Who Called Cops On Black Bird-Watcher Lo...</td>\n",
       "      <td>U.S. NEWS</td>\n",
       "      <td>Amy Cooper accused investment firm Franklin Te...</td>\n",
       "      <td>Nina Golgowski</td>\n",
       "      <td>2022-09-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                link  \\\n",
       "0  https://www.huffpost.com/entry/covid-boosters-...   \n",
       "1  https://www.huffpost.com/entry/american-airlin...   \n",
       "2  https://www.huffpost.com/entry/funniest-tweets...   \n",
       "3  https://www.huffpost.com/entry/funniest-parent...   \n",
       "4  https://www.huffpost.com/entry/amy-cooper-lose...   \n",
       "\n",
       "                                            headline   category  \\\n",
       "0  Over 4 Million Americans Roll Up Sleeves For O...  U.S. NEWS   \n",
       "1  American Airlines Flyer Charged, Banned For Li...  U.S. NEWS   \n",
       "2  23 Of The Funniest Tweets About Cats And Dogs ...     COMEDY   \n",
       "3  The Funniest Tweets From Parents This Week (Se...  PARENTING   \n",
       "4  Woman Who Called Cops On Black Bird-Watcher Lo...  U.S. NEWS   \n",
       "\n",
       "                                   short_description               authors  \\\n",
       "0  Health experts said it is too early to predict...  Carla K. Johnson, AP   \n",
       "1  He was subdued by passengers and crew when he ...        Mary Papenfuss   \n",
       "2  \"Until you have a dog you don't understand wha...         Elyse Wanshel   \n",
       "3  \"Accidentally put grown-up toothpaste on my to...      Caroline Bologna   \n",
       "4  Amy Cooper accused investment firm Franklin Te...        Nina Golgowski   \n",
       "\n",
       "        date  \n",
       "0 2022-09-23  \n",
       "1 2022-09-23  \n",
       "2 2022-09-23  \n",
       "3 2022-09-23  \n",
       "4 2022-09-22  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_json('news.json', lines=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "link                 209527\n",
       "headline             209527\n",
       "category             209527\n",
       "short_description    209527\n",
       "authors              209527\n",
       "date                 209527\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "link                         object\n",
       "headline                     object\n",
       "category                     object\n",
       "short_description            object\n",
       "authors                      object\n",
       "date                 datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories_list = df['category'].unique()\n",
    "\n",
    "len(categories_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['U.S. NEWS', 'COMEDY', 'PARENTING', 'WORLD NEWS', 'CULTURE & ARTS',\n",
       "       'TECH', 'SPORTS', 'ENTERTAINMENT', 'POLITICS', 'WEIRD NEWS',\n",
       "       'ENVIRONMENT', 'EDUCATION', 'CRIME', 'SCIENCE', 'WELLNESS',\n",
       "       'BUSINESS', 'STYLE & BEAUTY', 'FOOD & DRINK', 'MEDIA',\n",
       "       'QUEER VOICES', 'HOME & LIVING', 'WOMEN', 'BLACK VOICES', 'TRAVEL',\n",
       "       'MONEY', 'RELIGION', 'LATINO VOICES', 'IMPACT', 'WEDDINGS',\n",
       "       'COLLEGE', 'PARENTS', 'ARTS & CULTURE', 'STYLE', 'GREEN', 'TASTE',\n",
       "       'HEALTHY LIVING', 'THE WORLDPOST', 'GOOD NEWS', 'WORLDPOST',\n",
       "       'FIFTY', 'ARTS', 'DIVORCE'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tratamento da base de dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remoção de duplicatas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "link                 13\n",
       "headline             13\n",
       "category             13\n",
       "short_description    13\n",
       "authors              13\n",
       "date                 13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_rows = df[df.duplicated()]\n",
    "duplicate_rows.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "link                 209514\n",
       "headline             209514\n",
       "category             209514\n",
       "short_description    209514\n",
       "authors              209514\n",
       "date                 209514\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop_duplicates()\n",
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "476"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated(subset=['headline', 'short_description']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\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>link</th>\n",
       "      <th>headline</th>\n",
       "      <th>category</th>\n",
       "      <th>short_description</th>\n",
       "      <th>authors</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109100</th>\n",
       "      <td>https://www.huffingtonpost.com/entry/disney-ce...</td>\n",
       "      <td></td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td></td>\n",
       "      <td>Gary Snyder, ContributorWriter and Media Strat...</td>\n",
       "      <td>2015-01-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90944</th>\n",
       "      <td>https://www.huffingtonpost.com/entry/lincoln-2...</td>\n",
       "      <td></td>\n",
       "      <td>POLITICS</td>\n",
       "      <td></td>\n",
       "      <td>Robert Moran, ContributorRobert Moran leads Br...</td>\n",
       "      <td>2015-08-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110153</th>\n",
       "      <td>https://www.huffingtonpost.com/entry/beverly-h...</td>\n",
       "      <td></td>\n",
       "      <td>MEDIA</td>\n",
       "      <td></td>\n",
       "      <td>Gary Snyder, ContributorWriter and Media Strat...</td>\n",
       "      <td>2015-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103675</th>\n",
       "      <td>https://www.huffingtonpost.com/entry/us-and-eu...</td>\n",
       "      <td></td>\n",
       "      <td>WORLDPOST</td>\n",
       "      <td></td>\n",
       "      <td>Natasha Srdoc, ContributorAuthor, Economist, C...</td>\n",
       "      <td>2015-03-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122145</th>\n",
       "      <td>https://www.huffingtonpost.com/entry/beverly-h...</td>\n",
       "      <td></td>\n",
       "      <td>QUEER VOICES</td>\n",
       "      <td></td>\n",
       "      <td>Gary Snyder, ContributorWriter and Media Strat...</td>\n",
       "      <td>2014-08-28</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>192998</th>\n",
       "      <td>https://www.huffingtonpost.com/entry/why-you-s...</td>\n",
       "      <td>Why You Should Watch The Summer Olympics With ...</td>\n",
       "      <td>PARENTING</td>\n",
       "      <td>Every parent wants his child to learn sportsma...</td>\n",
       "      <td>Betsy Brown Braun, Contributor\\nChild developm...</td>\n",
       "      <td>2012-07-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19009</th>\n",
       "      <td>https://www.huffingtonpost.com/entry/octavia-s...</td>\n",
       "      <td>With This 'Shape Of Water' Clip, You'll Fall F...</td>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>Spencer and Sally Hawkins play 1960s janitors ...</td>\n",
       "      <td>Matthew Jacobs</td>\n",
       "      <td>2017-12-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18974</th>\n",
       "      <td>https://www.huffingtonpost.com/entry/the-shape...</td>\n",
       "      <td>With This 'Shape Of Water' Clip, You'll Fall F...</td>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>Spencer and Sally Hawkins play 1960s janitors ...</td>\n",
       "      <td>Matthew Jacobs</td>\n",
       "      <td>2017-12-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196025</th>\n",
       "      <td>https://www.huffingtonpost.com/entry/yves-sain...</td>\n",
       "      <td>Yves Saint Laurent Name Change: No More 'Yves'...</td>\n",
       "      <td>STYLE &amp; BEAUTY</td>\n",
       "      <td>According to Women's Wear Daily, Hedi Slimane ...</td>\n",
       "      <td></td>\n",
       "      <td>2012-06-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196043</th>\n",
       "      <td>https://www.huffingtonpost.com/entry/yves-sain...</td>\n",
       "      <td>Yves Saint Laurent Name Change: No More 'Yves'...</td>\n",
       "      <td>STYLE &amp; BEAUTY</td>\n",
       "      <td>According to Women's Wear Daily, Hedi Slimane ...</td>\n",
       "      <td></td>\n",
       "      <td>2012-06-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>673 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     link  \\\n",
       "109100  https://www.huffingtonpost.com/entry/disney-ce...   \n",
       "90944   https://www.huffingtonpost.com/entry/lincoln-2...   \n",
       "110153  https://www.huffingtonpost.com/entry/beverly-h...   \n",
       "103675  https://www.huffingtonpost.com/entry/us-and-eu...   \n",
       "122145  https://www.huffingtonpost.com/entry/beverly-h...   \n",
       "...                                                   ...   \n",
       "192998  https://www.huffingtonpost.com/entry/why-you-s...   \n",
       "19009   https://www.huffingtonpost.com/entry/octavia-s...   \n",
       "18974   https://www.huffingtonpost.com/entry/the-shape...   \n",
       "196025  https://www.huffingtonpost.com/entry/yves-sain...   \n",
       "196043  https://www.huffingtonpost.com/entry/yves-sain...   \n",
       "\n",
       "                                                 headline        category  \\\n",
       "109100                                                           BUSINESS   \n",
       "90944                                                            POLITICS   \n",
       "110153                                                              MEDIA   \n",
       "103675                                                          WORLDPOST   \n",
       "122145                                                       QUEER VOICES   \n",
       "...                                                   ...             ...   \n",
       "192998  Why You Should Watch The Summer Olympics With ...       PARENTING   \n",
       "19009   With This 'Shape Of Water' Clip, You'll Fall F...   ENTERTAINMENT   \n",
       "18974   With This 'Shape Of Water' Clip, You'll Fall F...   ENTERTAINMENT   \n",
       "196025  Yves Saint Laurent Name Change: No More 'Yves'...  STYLE & BEAUTY   \n",
       "196043  Yves Saint Laurent Name Change: No More 'Yves'...  STYLE & BEAUTY   \n",
       "\n",
       "                                        short_description  \\\n",
       "109100                                                      \n",
       "90944                                                       \n",
       "110153                                                      \n",
       "103675                                                      \n",
       "122145                                                      \n",
       "...                                                   ...   \n",
       "192998  Every parent wants his child to learn sportsma...   \n",
       "19009   Spencer and Sally Hawkins play 1960s janitors ...   \n",
       "18974   Spencer and Sally Hawkins play 1960s janitors ...   \n",
       "196025  According to Women's Wear Daily, Hedi Slimane ...   \n",
       "196043  According to Women's Wear Daily, Hedi Slimane ...   \n",
       "\n",
       "                                                  authors       date  \n",
       "109100  Gary Snyder, ContributorWriter and Media Strat... 2015-01-25  \n",
       "90944   Robert Moran, ContributorRobert Moran leads Br... 2015-08-22  \n",
       "110153  Gary Snyder, ContributorWriter and Media Strat... 2015-01-13  \n",
       "103675  Natasha Srdoc, ContributorAuthor, Economist, C... 2015-03-29  \n",
       "122145  Gary Snyder, ContributorWriter and Media Strat... 2014-08-28  \n",
       "...                                                   ...        ...  \n",
       "192998  Betsy Brown Braun, Contributor\\nChild developm... 2012-07-24  \n",
       "19009                                      Matthew Jacobs 2017-12-05  \n",
       "18974                                      Matthew Jacobs 2017-12-05  \n",
       "196025                                                    2012-06-21  \n",
       "196043                                                    2012-06-21  \n",
       "\n",
       "[673 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mostrando duplicatas \n",
    "df[df.duplicated(subset=['headline', 'short_description'], keep=False)].sort_values('headline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates(subset=['headline', 'short_description'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "link                 209038\n",
       "headline             209038\n",
       "category             209038\n",
       "short_description    209038\n",
       "authors              209038\n",
       "date                 209038\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Células vazias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "link                 2\n",
       "headline             2\n",
       "category             2\n",
       "short_description    2\n",
       "authors              2\n",
       "date                 2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando se há string vazia em 'headline'\n",
    "headline_empty = df[df['headline'] == '']\n",
    "headline_empty.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "link                 209036\n",
       "headline             209036\n",
       "category             209036\n",
       "short_description    209036\n",
       "authors              209036\n",
       "date                 209036\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removendo linhas com string vazia em 'headline'\n",
    "df = df[df['headline'] != '']\n",
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "link                 19610\n",
       "headline             19610\n",
       "category             19610\n",
       "short_description    19610\n",
       "authors              19610\n",
       "date                 19610\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando se há string vazia em 'short_description'\n",
    "short_description_empty = df[df['short_description'] == '']\n",
    "short_description_empty.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removendo linhas com string vazia em 'short_description'\n",
    "df = df[df['short_description'] != '']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "link                 189426\n",
       "headline             189426\n",
       "category             189426\n",
       "short_description    189426\n",
       "authors              189426\n",
       "date                 189426\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categorias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category\n",
       "POLITICS          32427\n",
       "WELLNESS          17937\n",
       "ENTERTAINMENT     14772\n",
       "STYLE & BEAUTY     9667\n",
       "TRAVEL             9417\n",
       "PARENTING          8762\n",
       "FOOD & DRINK       6330\n",
       "QUEER VOICES       5601\n",
       "HEALTHY LIVING     5264\n",
       "BUSINESS           5129\n",
       "COMEDY             4642\n",
       "SPORTS             4414\n",
       "HOME & LIVING      4285\n",
       "BLACK VOICES       4173\n",
       "THE WORLDPOST      3664\n",
       "WEDDINGS           3653\n",
       "PARENTS            3516\n",
       "DIVORCE            3423\n",
       "WORLD NEWS         3295\n",
       "WOMEN              3136\n",
       "IMPACT             3085\n",
       "CRIME              2831\n",
       "MEDIA              2402\n",
       "WEIRD NEWS         2316\n",
       "TECH               2054\n",
       "GREEN              2044\n",
       "TASTE              1940\n",
       "RELIGION           1877\n",
       "SCIENCE            1803\n",
       "MONEY              1754\n",
       "STYLE              1564\n",
       "ENVIRONMENT        1441\n",
       "U.S. NEWS          1377\n",
       "ARTS & CULTURE     1338\n",
       "WORLDPOST          1242\n",
       "CULTURE & ARTS     1062\n",
       "FIFTY              1042\n",
       "GOOD NEWS          1039\n",
       "LATINO VOICES      1022\n",
       "COLLEGE             921\n",
       "EDUCATION           902\n",
       "ARTS                863\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories_count = df['category'].value_counts()\n",
    "categories_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizando a quantidade de notícias por categoria\n",
    "df['category'].value_counts().plot(kind='bar', figsize=(10, 5))\n",
    "plt.title('Quantidade de notícias por categoria')\n",
    "plt.xlabel('Categoria')\n",
    "plt.ylabel('Quantidade')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = categories_count.plot.hist(bins=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category\n",
       "POLITICS          32427\n",
       "WELLNESS          17937\n",
       "ENTERTAINMENT     14772\n",
       "STYLE & BEAUTY     9667\n",
       "TRAVEL             9417\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecionando as 5 categorias com mais notícias\n",
    "top_categories = categories_count[:5].index\n",
    "top_categories\n",
    "\n",
    "# Filtrando as notícias das 5 categorias com mais notícias\n",
    "df = df[df['category'].isin(top_categories)]\n",
    "df['category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "link                 84220\n",
       "headline             84220\n",
       "category             84220\n",
       "short_description    84220\n",
       "authors              84220\n",
       "date                 84220\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantidade de palavras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>link</th>\n",
       "      <th>headline</th>\n",
       "      <th>category</th>\n",
       "      <th>short_description</th>\n",
       "      <th>authors</th>\n",
       "      <th>date</th>\n",
       "      <th>word_length_headline</th>\n",
       "      <th>word_length_description</th>\n",
       "      <th>total_word_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>https://www.huffpost.com/entry/golden-globes-r...</td>\n",
       "      <td>Golden Globes Returning To NBC In January Afte...</td>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>For the past 18 months, Hollywood has effectiv...</td>\n",
       "      <td></td>\n",
       "      <td>2022-09-20</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>https://www.huffpost.com/entry/biden-us-forces...</td>\n",
       "      <td>Biden Says U.S. Forces Would Defend Taiwan If ...</td>\n",
       "      <td>POLITICS</td>\n",
       "      <td>President issues vow as tensions with China rise.</td>\n",
       "      <td></td>\n",
       "      <td>2022-09-19</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>https://www.huffpost.com/entry/ukraine-festiva...</td>\n",
       "      <td>‘Beautiful And Sad At The Same Time’: Ukrainia...</td>\n",
       "      <td>POLITICS</td>\n",
       "      <td>An annual celebration took on a different feel...</td>\n",
       "      <td>Jonathan Nicholson</td>\n",
       "      <td>2022-09-19</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>https://www.huffpost.com/entry/james-cameron-f...</td>\n",
       "      <td>James Cameron Says He 'Clashed' With Studio Be...</td>\n",
       "      <td>ENTERTAINMENT</td>\n",
       "      <td>The \"Avatar\" director said aspects of his 2009...</td>\n",
       "      <td>Ben Blanchet</td>\n",
       "      <td>2022-09-18</td>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>https://www.huffpost.com/entry/europe-britain-...</td>\n",
       "      <td>Biden Says Queen's Death Left 'Giant Hole' For...</td>\n",
       "      <td>POLITICS</td>\n",
       "      <td>U.S. President Joe Biden, in London for the fu...</td>\n",
       "      <td>Darlene Superville, AP</td>\n",
       "      <td>2022-09-18</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 link  \\\n",
       "20  https://www.huffpost.com/entry/golden-globes-r...   \n",
       "21  https://www.huffpost.com/entry/biden-us-forces...   \n",
       "24  https://www.huffpost.com/entry/ukraine-festiva...   \n",
       "28  https://www.huffpost.com/entry/james-cameron-f...   \n",
       "30  https://www.huffpost.com/entry/europe-britain-...   \n",
       "\n",
       "                                             headline       category  \\\n",
       "20  Golden Globes Returning To NBC In January Afte...  ENTERTAINMENT   \n",
       "21  Biden Says U.S. Forces Would Defend Taiwan If ...       POLITICS   \n",
       "24  ‘Beautiful And Sad At The Same Time’: Ukrainia...       POLITICS   \n",
       "28  James Cameron Says He 'Clashed' With Studio Be...  ENTERTAINMENT   \n",
       "30  Biden Says Queen's Death Left 'Giant Hole' For...       POLITICS   \n",
       "\n",
       "                                    short_description                 authors  \\\n",
       "20  For the past 18 months, Hollywood has effectiv...                           \n",
       "21  President issues vow as tensions with China rise.                           \n",
       "24  An annual celebration took on a different feel...      Jonathan Nicholson   \n",
       "28  The \"Avatar\" director said aspects of his 2009...            Ben Blanchet   \n",
       "30  U.S. President Joe Biden, in London for the fu...  Darlene Superville, AP   \n",
       "\n",
       "         date  word_length_headline  word_length_description  \\\n",
       "20 2022-09-20                    10                       25   \n",
       "21 2022-09-19                    10                        8   \n",
       "24 2022-09-19                    17                       15   \n",
       "28 2022-09-18                    10                       19   \n",
       "30 2022-09-18                    10                       30   \n",
       "\n",
       "    total_word_length  \n",
       "20                 35  \n",
       "21                 18  \n",
       "24                 32  \n",
       "28                 29  \n",
       "30                 40  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculando a quantidade de palavras em cada notícia e colocando em uma nova coluna\n",
    "df_word_length = df.copy()\n",
    "df_word_length['word_length_headline'] = df['headline'].apply(lambda x: len(x.split()))\n",
    "df_word_length['word_length_description'] = df['short_description'].apply(lambda x: len(x.split()))\n",
    "df_word_length['total_word_length'] = df_word_length['word_length_headline'] + df_word_length['word_length_description']\n",
    "\n",
    "df_word_length.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizando a quantidade de palavras em cada manchete\n",
    "\n",
    "interval = (0,30)\n",
    "\n",
    "df_word_length['word_length_headline'].plot(kind='hist', bins=30, figsize=(10, 5), range=interval)\n",
    "plt.title('Quantidade de palavras em cada manchete')\n",
    "plt.xlabel('Quantidade de palavras')\n",
    "plt.ylabel('Frequência')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    84220.000000\n",
       "mean         9.757172\n",
       "std          3.137350\n",
       "min          1.000000\n",
       "25%          8.000000\n",
       "50%         10.000000\n",
       "75%         12.000000\n",
       "max         44.000000\n",
       "Name: word_length_headline, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_word_length['word_length_headline'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot da quantidade de palavras em cada manchete\n",
    "df_word_length['word_length_headline'].plot(kind='box', figsize=(10, 5))\n",
    "plt.title('Boxplot da quantidade de palavras em cada manchete')\n",
    "plt.ylabel('Quantidade de palavras')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizando a quantidade de palavras em cada descrição\n",
    "\n",
    "interval = (0, 80)\n",
    "\n",
    "df_word_length['word_length_description'].plot(kind='hist', bins=70, figsize=(10, 5), color='red', range=interval)\n",
    "plt.title('Quantidade de palavras em cada descrição')\n",
    "plt.xlabel('Quantidade de palavras')\n",
    "plt.ylabel('Frequência')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    84220.000000\n",
       "mean        21.532997\n",
       "std         13.226110\n",
       "min          1.000000\n",
       "25%         13.000000\n",
       "50%         20.000000\n",
       "75%         25.000000\n",
       "max        243.000000\n",
       "Name: word_length_description, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_word_length['word_length_description'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot da quantidade de palavras em cada descrição\n",
    "df_word_length['word_length_description'].plot(kind='box', figsize=(10, 5), color='red')\n",
    "plt.title('Boxplot da quantidade de palavras em cada descrição')\n",
    "plt.ylabel('Quantidade de palavras')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mostrando o total de palavras em todas as manchetes, com descrição e manchetes\n",
    "\n",
    "interval = (0, 80)\n",
    "\n",
    "df_word_length['total_word_length'].plot(kind='hist', bins=70, figsize=(10, 5), color='green', range=interval)\n",
    "plt.title('Quantidade de palavras em cada notícia')\n",
    "plt.xlabel('Quantidade de palavras')\n",
    "plt.ylabel('Frequência')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    84220.000000\n",
       "mean        31.290169\n",
       "std         12.562055\n",
       "min          3.000000\n",
       "25%         23.000000\n",
       "50%         29.000000\n",
       "75%         36.000000\n",
       "max        245.000000\n",
       "Name: total_word_length, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando a quantidade de palavras em cada notícia\n",
    "df_word_length['total_word_length'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vzs7OOn78uCpWrCgvLy+L5+YOlTt+/Li5zdnZWf/73//UrFkzubq6at68eQX+InVwcLAohqRrf+RJuuFMe+fPn1daWppq166db1toaKhycnJ08uRJ1atXz7oX///l5l/Qeii1a9fO90fMsmXL9Oabb2rnzp26evWquf3vht7c6DhOTk75zoUtTpw4ofHjx+uHH37Idw1JcnKyxWNXV9d8fxiXLVv2li4el669JgcHB9WsWdOivaCfUXp6uqZMmaJ58+bp9OnTFtfD5c3z+PHj5j96b7bPw4cPS7r2B3NBvL29/zb3ojhOrut/7p6engoKCrJ4v+/du1evvfaa1q1bl+9Lget/ltez9n3Zu3dvvfrqqzp9+rQqVaqk9evX69y5c+rdu3e+fQYHBxd4rAULFui9997TgQMHlJmZWWD88OHD9c033+jBBx9UpUqV9MADD+jRRx9Vp06dbvo6Dh8+rOTkZPn7+xe4/dy5cxaP8xZLksxfSlw/VDa3/e/e67GxserZs+dNYyTrzsHx48cVFBQkT09Pi+cW9P8jKSlJkyZN0qJFi/K9xr/72ee6/lyULVtW0v+95tzPoIKOX6dOHW3atMmq4+R1/vx5paSkqH79+jY/15bPLuBOQyEF3IEcHBzUvn17TZ8+XYcPH7a5KJGkVatWSbr2rerhw4dv+MfY7Sg6OloPPfSQ2rZtq1mzZikoKEhOTk6aN2+eFi5caLfj3Kgou37SgezsbN1///1KSkrSyy+/rDp16sjDw0OnT5/WwIED802ckPc6taI2atQozZs3T88//7xatWolHx8fmUwmPfbYYzec4OFmcp/z+eefW1zvk8teswsW1XEuXryodu3aydvbW5MnT1bNmjXl6uqq33//XS+//PJNz5Et78vevXtr3Lhx+vbbb/X888/rm2++kY+PT4EFTkHXi33xxRcaOHCgIiMj9eKLL8rf31+Ojo6aMmWKYmNjzXH+/v7auXOnVq1apRUrVmjFihWaN2+e+vfvrwULFtzwteTk5Mjf319ffvllgdsL6iEryI3aDSsmtPk71p4DWzz66KPasmWLXnzxRTVq1Eienp7KyclRp06drP7/UZiv2d5s/ewC7jQUUsAdKisrS9K1i64lqVq1avr555916dIli16p3KFG1apVM7ft2rVLkydP1qBBg7Rz504NHjxYu3fvzjd0LScnR0ePHjX3QknSoUOHJOmGF+5XqFBB7u7uOnjwYL5tBw4ckIODg/lbaGsuzM6Vm39uz0Ne1x9ryZIlcnV11apVq+Ti4mJunzdvnk3Hydu7kZmZqWPHjqlhw4bmttxvkq+f3Stv758k7d69W4cOHdKCBQvUv39/c7utw6nysvXc5eTkKDY21uJb7oJ+RosXL9aAAQP03nvvmduuXLmS7zVWq1bNqp9Fbi+Yv7+/OnToYHXORX2cXIcPH7aYMS81NVXx8fHq3LmzpGuzml24cEFRUVEWa+0cO3bsb/dty/syODhYzZs319dff62RI0cqKipKkZGRFs+7mcWLF6tGjRqKioqyeK9MmDAhX6yzs7O6deumbt26KScnR8OHD9fHH3+s119/XSEhIQXuv2bNmvr555/VunXrAgu5wlazZk3t2bPnpjHWnoNq1app7dq1Sk1NteiVuv499tdff2nt2rWaNGmSxo8fb24v6P35T+R+Bh08eDBfD+vBgwctPsut/RyoUKGCvL29//acXa8wPruA2wnXSAF3oMzMTK1evVrOzs7moXudO3dWdna2ZsyYYRE7bdo0mUwmPfjgg+bnDhw4UBUrVtT06dM1f/58nT17VqNHjy7wWHn3ZxiGZsyYIScnJ913330Fxjs6OuqBBx7Q999/bzEc6uzZs1q4cKHatGljHmbl4eEhKX8hUpCgoCA1atRICxYssBhOsmbNGu3bty9fDiaTyaJnKC4uTkuXLv3b4zRt2lQVKlTQnDlzzFMCS9dm0bo+z9w/3jdu3Ghuy87O1ty5c/PlI1l+42wYhlVTTN9I7ux51py73J/9hx9+aNH+wQcf5It1dHTM9834Rx99lK+XrXPnztq6dat+++03c9v58+fz9VB07NhR3t7eeuuttyyGVuV9zs0U1XFyzZ071+L5s2fPVlZWlvkcFvSzzMjI0KxZs/5237a+L3v37q2tW7fqf//7nxITEwsc1nezY12f56+//qqYmBiLuOun7XZwcDDP/ph36OH1Hn30UWVnZ+uNN97Ity0rK8uq9+U/0bNnT/35558FLn+Q+5qtPQedO3dWVlaWZs+ebW7Lzs7WRx99ZBFX0P6kgv8f/RNNmzaVv7+/5syZY/EzWLFihfbv328x26a1n6EODg6KjIzUjz/+qO3bt+fbfqPesML47AJuJ/RIAXeAFStWmHuWzp07p4ULF+rw4cN65ZVXzEVJt27d1L59e/3rX/9SXFycGjZsqNWrV+v777/X888/b/6jP/f6jLVr18rLy0t33323xo8fr9dee029evUyf/MuXbtWZ+XKlRowYIBatGihFStW6KefftKrr76ab+hOXm+++abWrFmjNm3aaPjw4SpTpow+/vhjXb16Ve+88445rlGjRnJ0dNTbb7+t5ORkubi46N57773hdRdTpkxRly5d1KZNGz355JNKSkrSRx99pHr16pl75qRrF2W///776tSpk/r27atz585p5syZCgkJ0a5du256rp2cnPTmm29q6NChuvfee9W7d28dO3ZM8+bNy3eNVL169dSyZUuNGzdOSUlJ8vPz06JFi8y9hbnq1KmjmjVrauzYsTp9+rS8vb21ZMmSW77mSbo2nKtu3br6+uuvddddd8nPz0/169cv8BqIRo0aqU+fPpo1a5aSk5MVFhamtWvXFrh2TdeuXfX555/Lx8dHdevWVUxMjH7++WeVK1fOIu6ll17S559/rk6dOum5554zT0terVo1i3Ps7e2t2bNn64knnlDjxo312GOPqUKFCjpx4oR++ukntW7dOl/xXxzHyZWRkaH77rvPPAX3rFmz1KZNGz300EOSpLCwMJUtW1YDBgzQs88+K5PJpM8//9yqYVm2vi8fffRRjR07VmPHjpWfn59NPW1du3ZVVFSUunfvri5duujYsWOaM2eO6tata/F/ZfDgwUpKStK9996rypUr6/jx4/roo4/UqFGjmy5F0K5dOw0dOlRTpkzRzp079cADD8jJyUmHDx/Wt99+q+nTp5vXuyoML774ohYvXqxHHnlETz75pJo0aaKkpCT98MMPmjNnjho2bGj1OejWrZtat26tV155RXFxcapbt66ioqLyXf/j7e2ttm3b6p133lFmZqYqVaqk1atXW9UbaQsnJye9/fbbGjRokNq1a6c+ffqYpz+vXr26xZdeTZo0kXRtIpmOHTvK0dEx3/T4ud566y2tXr1a7dq105AhQxQaGqr4+Hh9++232rRpk3x9ffM9pzA+u4DbShHPEgjAjgqa/tzV1dVo1KiRMXv2bPOUtbkuXbpkjB492qhYsaLh5ORk1KpVy3j33XfNcTt27DDKlCljMaW5YVyb6rdZs2ZGxYoVjb/++sswjGtTN3t4eBixsbHGAw88YLi7uxsBAQHGhAkTjOzsbIvnq4BpuH///XejY8eOhqenp+Hu7m60b9/eYvrjXJ988olRo0YNw9HR0appj5csWWKEhoYaLi4uRt26dY2oqKgCp5n+73//a9SqVctwcXEx6tSpY8ybNy/ftOA3M2vWLCM4ONhwcXExmjZtamzcuNFo166dxfTnhmEYsbGxRocOHQwXFxcjICDAePXVV401a9bkey379u0zOnToYHh6ehrly5c3nn76aePPP//MN71y7nm/XkG5b9myxWjSpInh7Oxs8TMoKDY9Pd149tlnjXLlyhkeHh5Gt27djJMnT+b72f3111/GoEGDjPLlyxuenp5Gx44djQMHDhjVqlUzBgwYYLHPXbt2Ge3atTNcXV2NSpUqGW+88Ybx3//+94ZTwnfs2NHw8fExXF1djZo1axoDBw40tm/fftOfQ1EdJ/f/2oYNG4whQ4YYZcuWNTw9PY1+/foZFy5csIjdvHmz0bJlS8PNzc2oWLGi8dJLL5mn8877M7fH+7J169aGJGPw4MH5tuVOf/7uu+/m25aTk2O89dZbRrVq1QwXFxfjnnvuMZYtW5Yvp8WLFxsPPPCA4e/vbzg7OxtVq1Y1hg4dasTHx9/0fOWaO3eu0aRJE8PNzc3w8vIyGjRoYLz00kvGmTNnzDHVqlUzunTpku+5kowRI0ZY/Zqud+HCBWPkyJFGpUqVDGdnZ6Ny5crGgAEDjMTERJvOQe6+nnjiCcPb29vw8fExnnjiCeOPP/7I9//z1KlTRvfu3Q1fX1/Dx8fHeOSRR4wzZ87ccCmCvHKnFv/2228LfM15j2MYhvH1118b99xzj+Hi4mL4+fkZ/fr1M06dOmURk5WVZYwaNcqoUKGCYTKZLN5HBeV0/Phxo3///kaFChUMFxcXo0aNGsaIESPM07EXNP25tZ9dwJ3IZBgl8OpFACXewIEDtXjxYotvbku7iIgISdeuk8GdJXcB1G3bthU4PTQAoPThGikAAAAAsBGFFAAAAADYiEIKAAAAAGzENVIAAAAAYCN6pAAAAADARhRSAAAAAGAjFuSVlJOTozNnzsjLy0smk6m40wEAAABQTAzD0KVLl1SxYkU5ONy434lCStKZM2dUpUqV4k4DAAAAQAlx8uRJVa5c+YbbKaQkeXl5Sbp2sry9vYs5GwAAAADFJSUlRVWqVDHXCDdCISWZh/N5e3tTSAEAAAD420t+mGwCAAAAAGxEIQUAAAAANqKQAgAAAAAbUUgBAAAAgI2KtZCaMmWKmjVrJi8vL/n7+ysyMlIHDx60iImIiJDJZLK4PfPMMxYxJ06cUJcuXeTu7i5/f3+9+OKLysrKKsqXAgAAAKAUKdZZ+zZs2KARI0aoWbNmysrK0quvvqoHHnhA+/btk4eHhznu6aef1uTJk82P3d3dzfezs7PVpUsXBQYGasuWLYqPj1f//v3l5OSkt956q0hfDwAAAIDSwWQYhlHcSeQ6f/68/P39tWHDBrVt21bStR6pRo0a6YMPPijwOStWrFDXrl115swZBQQESJLmzJmjl19+WefPn5ezs3O+51y9elVXr141P86dKz45OZnpzwEAAIBSLCUlRT4+Pn9bG5Soa6SSk5MlSX5+fhbtX375pcqXL6/69etr3LhxSktLM2+LiYlRgwYNzEWUJHXs2FEpKSnau3dvgceZMmWKfHx8zLcqVaoUwqsBAAAAcKcqMQvy5uTk6Pnnn1fr1q1Vv359c3vfvn1VrVo1VaxYUbt27dLLL7+sgwcPKioqSpKUkJBgUURJMj9OSEgo8Fjjxo3TmDFjzI9ze6QAAAAAwBolppAaMWKE9uzZo02bNlm0DxkyxHy/QYMGCgoK0n333afY2FjVrFnzlo7l4uIiFxeXf5QvAAAAgNKrRAztGzlypJYtW6ZffvlFlStXvmlsixYtJElHjhyRJAUGBurs2bMWMbmPAwMDCyFbAMCdJjs7W+vXr9dXX32l9evXKzs7u7hTAgCUcMVaSBmGoZEjR+q7777TunXrFBwc/LfP2blzpyQpKChIktSqVSvt3r1b586dM8esWbNG3t7eqlu3bqHkDQC4c0RFRSkkJETt27dX37591b59e4WEhJiHkAMAUJBiLaRGjBihL774QgsXLpSXl5cSEhKUkJCg9PR0SVJsbKzeeOMN7dixQ3Fxcfrhhx/Uv39/tW3bVnfffbck6YEHHlDdunX1xBNP6M8//9SqVav02muvacSIEQzfAwDcVFRUlHr16qUGDRooJiZGly5dMk9i1KtXL4opAMANFev05yaTqcD2efPmaeDAgTp58qQef/xx7dmzR5cvX1aVKlXUvXt3vfbaaxZTER4/flzDhg3T+vXr5eHhoQEDBug///mPypSx7hIwa6c4BADcObKzsxUSEqIGDRpoyZIl2rx5s+Lj4xUUFKTWrVurZ8+e2rNnjw4fPixHR8fiThcAUESsrQ2KdbKJv6vhqlSpog0bNvztfqpVq6bly5fbKy0AQCkQHR2tuLg4DR06VHfddZfi4uLM26pXr64hQ4boxx9/VHR0tCIiIootTwBAyVQiJpsAAKCoxcfHS7q2JEZBQ/teffVVizgAAPIqMdOfAwBQlPz9/SVJbdq00dKlS+XgcO27xZYtW2rp0qVq27atNm/ebI4DACAveqQAACjAja7jBQBAopACAJRSuctmbN68WZGRkRZD+yIjI7V582aLOAAA8qKQAgCUSrnrEb711lvavXu3wsLC5O3trbCwMO3Zs0f//ve/LeIAAMiLa6QAAKVSeHi4qlevri1btujQoUMFTn8eHBys8PDw4k4VAFAC0SMFACiVHB0d9d5772nZsmXq2bOnXFxc1LVrV7m4uKhnz55atmyZpk6dyhpSAIAC0SMFACi1evToocWLF+uFF15QWFiYuT04OFiLFy9Wjx49ijE7AEBJZjL+blXcUsDa1YsBAHem7OxsRUdHm4f2hYeH0xMFAKWUtbUBPVIAgFLP0dFRERERxZ0GAOA2wjVSAAAAAGAjCikAAAAAsBGFFAAAAADYiEIKAAAAAGxEIQUAAAAANqKQAgAAAAAbUUgBAAAAgI0opAAAAADARhRSAAAAAGAjCikAAAAAsBGFFAAAAADYiEIKAAAAAGxEIQUAAAAANqKQAgAAAAAbUUgBAAAAgI0opAAAAADARhRSAAAAAGAjCikAAAAAsBGFFAAAAADYiEIKAAAAAGxEIQUAAAAANqKQAgAAAAAbUUgBAAAAgI0opAAAAADARhRSAAAAAGAjCikAAAAAsBGFFAAAAADYiEIKAAAAAGxEIQUAAAAANqKQAgAAAAAbUUgBAAAAgI0opAAAAADARhRSAAAAAGAjCikAAAAAsBGFFAAAAADYqExxJwAAQHHLzs5WdHS04uPjFRQUpPDwcDk6OhZ3WgCAEoweKQBAqRYVFaWQkBC1b99effv2Vfv27RUSEqKoqKjiTg0AUIJRSAEASq2oqCj16tVLDRo0UExMjC5duqSYmBg1aNBAvXr1opgCANyQyTAMo7iTKG4pKSny8fFRcnKyvL29izsdAEARyM7OVkhIiBo0aKClS5fKweH/vlvMyclRZGSk9uzZo8OHDzPMDwBKEWtrA3qkAAClUnR0tOLi4vTqq69aFFGS5ODgoHHjxunYsWOKjo4upgwBACUZhRQAoFSKj4+XJNWvX7/A7bntuXEAAORFIQUAKJWCgoIkSXv27Clwe257bhwAAHlxjZS4RgoASqO810gtWbJEmzdvNk9/3rp1a/Xs2ZNrpACgFLK2NmAdKQBAqeTo6Kj33ntPPXv2lI+Pj9LT083b3NzclJ6eriVLllBEAQAKxNA+AECpZjKZCmwrqB0AgFwM7RND+wCgNGJoHwCgIAztAwDgJnKnP//qq6/k5OSkiIgIi+3jxo1TWFiYoqOj820DAIChfQCAUonpzwEA/wSFFACgVGL6cwDAP0EhBQAolcLDw1W9enW99dZbysnJsdiWk5OjKVOmKDg4WOHh4cWUIQCgJKOQAgCUSrnTny9btkyRkZGKiYnRpUuXFBMTo8jISC1btkxTp05logkAQIGKtZCaMmWKmjVrJi8vL/n7+ysyMlIHDx60iLly5YpGjBihcuXKydPTUz179tTZs2ctYk6cOKEuXbrI3d1d/v7+evHFF5WVlVWULwUAcBvq0aOHFi9erN27dyssLEze3t4KCwvTnj17tHjxYvXo0aO4UwQAlFDFOv15p06d9Nhjj6lZs2bKysrSq6++qj179mjfvn3y8PCQJA0bNkw//fST5s+fLx8fH40cOVIODg7avHmzpGvT1zZq1EiBgYF69913FR8fr/79++vpp5/WW2+9ZVUeTH8OAKVbdna2oqOjzdOfh4eH0xMFAKWUtbVBiVpH6vz58/L399eGDRvUtm1bJScnq0KFClq4cKF69eolSTpw4IBCQ0MVExOjli1basWKFeratavOnDmjgIAASdKcOXP08ssv6/z583J2dv7b41JIAUDpRiEFAMhlbW1Qoq6RSk5OliT5+flJknbs2KHMzEx16NDBHFOnTh1VrVpVMTExkqSYmBg1aNDAXERJUseOHZWSkqK9e/cWeJyrV68qJSXF4gYAKJ2ioqIUEhKi9u3bq2/fvmrfvr1CQkIUFRVV3KkBAEqwElNI5eTk6Pnnn1fr1q3Na3ckJCTI2dlZvr6+FrEBAQFKSEgwx+QtonK3524ryJQpU+Tj42O+ValSxc6vBgBwO4iKilKvXr3UoEEDi8kmGjRooF69elFMAQBuqMQUUiNGjNCePXu0aNGiQj/WuHHjlJycbL6dPHmy0I8JAChZsrOz9cILL6hr165asmSJrly5oh9//FFXrlzRkiVL1LVrV40dO1bZ2dnFnSoAoAQqU9wJSNLIkSO1bNkybdy4UZUrVza3BwYGKiMjQxcvXrTolTp79qwCAwPNMb/99pvF/nJn9cuNuZ6Li4tcXFzs/CoAALeT6OhoxcXFaejQobrrrrsUFxdn3la9enUNGTJEP/74o6KjoxUREVFseQIASqZi7ZEyDEMjR47Ud999p3Xr1ik4ONhie5MmTeTk5KS1a9ea2w4ePKgTJ06oVatWkqRWrVpp9+7dOnfunDlmzZo18vb2Vt26dYvmhQAAbjvx8fGSpFdffbXAoX3/+te/LOIAAMirWHukRowYoYULF+r777+Xl5eX+ZomHx8fubm5ycfHR0899ZTGjBkjPz8/eXt7a9SoUWrVqpVatmwpSXrggQdUt25dPfHEE3rnnXeUkJCg1157TSNGjKDXCQBwQ/7+/pKk1q1ba+nSpXJwuPbdYsuWLbV06VK1a9dOmzZtMscBAJBXsRZSs2fPlqR8QybmzZungQMHSpKmTZsmBwcH9ezZU1evXlXHjh01a9Ysc6yjo6OWLVumYcOGqVWrVvLw8NCAAQM0efLkonoZAIDbXHZ2tjZu3Gie/rx169YqQauDAABKoGItpKz5JeXq6qqZM2dq5syZN4ypVq2ali9fbs/UAAB3uNwh4Zs2bZKPj4/S09PN29zc3MyP8w4dBwAgV4mZtQ8AgKIUFBR0w20mk8mqOABA6VUiZu0DAKCohYWFqUyZMipXrpyOHz+umJgY89C+Vq1aqVq1arpw4YLCwsKKO1UAQAlEjxQAoFTasmWLsrKydO7cOT3yyCNycXFR165d5eLiokceeUTnzp1TVlaWtmzZUtypAgBKIAopAECplDut+eeff67du3crLCxM3t7eCgsL0549e/T5559bxAEAkBdD+wAApVLutU81a9bUwYMHNWvWLMXGxqpmzZoaPny4duzYYREHAEBeJoP5XZWSkiIfHx8lJyfL29u7uNMBABSB7OxshYSEqHz58kpMTFRcXJx5W/Xq1VW+fHlduHBBhw8flqOjY/ElCgAoUtbWBgztAwCUSo6OjnrkkUe0fft2paena+7cuTpz5ozmzp2r9PR0bd++Xb169aKIAgAUiB4p0SMFAKVR3h6p8+fP6/jx4+Zt9EgBQOllbW3ANVIAgFIpOjpacXFx+uqrr9SsWTNFR0ebpz8PDw/Xb7/9prCwMEVHRysiIqK40wUAlDAUUgCAUil3Nr769evL0dExX7FUv359izgAAPLiGikAQKmUOxvfnj17Ctye286sfQCAglBIAQBKpfDwcFWvXl1vvfWWcnJyLLbl5ORoypQpCg4OVnh4eDFlCAAoySikAAClkqOjo9577z0tW7ZMkZGRiomJ0aVLlxQTE6PIyEgtW7ZMU6dOZaIJAECBuEYKAFBq9ejRQ4sXL9YLL7ygsLAwc3twcLAWL16sHj16FGN2AICSjOnPxfTnAFDaZWdn55u1j54oACidWJAXAAAAAAoJhRQAoFSLiopSSEiI2rdvr759+6p9+/YKCQlRVFRUcacGACjBuEYKAFBqRUVFqVevXurSpYtefPFFubm5KT09XStWrFCvXr24TgoAcENcIyWukQKA0ig7O1shISEqX768zp07pxMnTpi3Va1aVf7+/rpw4YIOHz7M9VIAUIpYWxvQIwUAKJWio6MVFxenuLg4ubm5WWw7f/68ubCKjo5WREREMWQIACjJKKQAAKXS6dOnzffvvfdede7c2Ty0b/ny5frpp5/yxQEAkMvmQio9PV2GYcjd3V2SdPz4cX333XeqW7euHnjgAbsnCABAYUhISJAkVa9eXXv27DEXTpJUrVo1VatWTcePHzfHAQCQl82F1MMPP6wePXromWee0cWLF9WiRQs5OTkpMTFR77//voYNG1YYeQIAYFdJSUmSpLi4OHXp0kUvvfRSgT1SuXEAAORlcyH1+++/a9q0aZKkxYsXKyAgQH/88YeWLFmi8ePHU0gBAG4769ats+iRuv6aKQAArmfzOlJpaWny8vKSJK1evVo9evSQg4ODWrZsqePHj9s9QQAACoOfn59d4wAApYvNhVRISIiWLl2qkydPatWqVebros6dO8fU4QCA24a/v7/5/vUrgeR9nDcOAIBcNhdS48eP19ixY1W9enW1aNFCrVq1knStd+qee+6xe4IAABSGCxcumO9fuXLFYlvex3njAADIZfM1Ur169VKbNm0UHx+vhg0bmtvvu+8+de/e3a7JAQBQWMqVK2e+7+rqalE85U46cX0cAAC5bmkdqcDAQAUGBlq0NW/e3C4JAQBQFM6dO2e+f++99yokJERXrlyRq6urjhw5ouXLl+eLAwAg1y0VUtu3b9c333yjEydOKCMjw2JbVFSUXRIDAKAw5U5rXrFiRa1atcpcOEmSo6OjgoKCFB8fz/TnAIAC2XyN1KJFixQWFqb9+/fru+++U2Zmpvbu3at169bJx8enMHIEAMDuHByu/Qo8c+aM/Pz8FBERobZt2yoiIkJ+fn6Kj4+3iAMAIC+be6TeeustTZs2TSNGjJCXl5emT5+u4OBgDR06VEFBQYWRIwAAdhceHi5JcnJy0vnz57V+/XqL7U5OTsrMzDTHAQCQl81fs8XGxqpLly6SJGdnZ12+fFkmk0mjR4/W3Llz7Z4gAACFwdHRUZKUmZlZ4Pbc9tw4AADysrmQKlu2rC5duiRJqlSpkvbs2SNJunjxotLS0uybHQAAheTMmTN2jQMAlC42F1Jt27bVmjVrJEmPPPKInnvuOT399NPq06eP7rvvPrsnCABAYdi8ebMkycXFRZUrV7bYVqVKFTk7O1vEAQCQl83XSM2YMcO81sa//vUvOTk5acuWLerZs6dee+01uycIAEBhyB1R4enpma/X6fTp0/L19VVSUpI5DgCAvGwqpLKysrRs2TJ17NhR0rWZjF555ZVCSQwAgKJw4cKFfG05OTlMew4AuCmbhvaVKVNGzzzzjMXq7wAA3I5yJ06yVxwAoHSx+Rqp5s2ba+fOnYWQCgAARSc7O9uucQCA0sVkGIZhyxO++eYbjRs3TqNHj1aTJk3k4eFhsf3uu++2a4JFISUlRT4+PkpOTpa3t3dxpwMAKALBwcGKi4v727jq1avr2LFjhZ8QAKBEsLY2sHmyiccee0yS9Oyzz5rbTCaTDMOQyWTimzsAwG3B2mHqDGcHABTE5kKKb+UAAHcCT09Pu8YBAEoXmwupgIAAubq6FkYuAAAUmVOnTtk1DgBQutg82YS/v78GDBigNWvWKCcnpzByAgCg0GVkZNg1DgBQuthcSC1YsEBpaWl6+OGHValSJT3//PPavn17YeQGAEChsfbLQL40BAAUxOZCqnv37vr222919uxZvfXWW9q3b59atmypu+66S5MnTy6MHAEAsDsHB+t+BVobBwAoXWye/rwg+/btU79+/bRr167bctY+pj8HgNLH2dlZmZmZfxvn5OTE8D4AKEWsrQ1u+Wu2K1eu6JtvvlFkZKQaN26spKQkvfjii7e6OwAAipSXl5dd4wAApYvNs/atWrVKCxcu1NKlS1WmTBn16tVLq1evVtu2bQsjPwAACkVaWppd4wAApYvNhVT37t3VtWtXffbZZ+rcubOcnJwKIy8AAAqVtUPRb8ch6wCAwmdzIXX27FmGOQAAbntubm5WXSPl5uZWBNkAAG43NhdSeYuoK1eu5LsAl8kaAAC3g/T0dLvGAQBKF5snm7h8+bJGjhwpf39/eXh4qGzZshY3AABuB9b0RtkSBwAoXWwupF566SWtW7dOs2fPlouLiz799FNNmjRJFStW1GeffVYYOQIAYHfOzs52jQMAlC42D+378ccf9dlnnykiIkKDBg1SeHi4QkJCVK1aNX355Zfq169fYeQJAIBdeXt7KzEx0ao4AACuZ3OPVFJSkmrUqCHp2i+XpKQkSVKbNm20ceNG+2YHAEAhuXz5sl3jAACli82FVI0aNXTs2DFJUp06dfTNN99IutZT5evra9fkAAAoLNYu38EyHwCAgthcSA0aNEh//vmnJOmVV17RzJkz5erqqtGjR+vFF1+0e4IAABSG5s2b2zUOAFC62HyN1OjRo833O3TooAMHDmjHjh0KCQnR3XffbdfkAAAoLLmjK+wVBwAoXWwupK5XrVo1VatWzR65AABQZFJSUuwaBwAoXawqpD788EOrd/jss8/ecjIAABSVsmXL6vz581bFAQBwPasKqWnTplm1M5PJZFMhtXHjRr377rvasWOH4uPj9d133ykyMtK8feDAgVqwYIHFczp27KiVK1eaHyclJWnUqFH68ccf5eDgoJ49e2r69Ony9PS0Og8AQOlz6dIlu8YBAEoXqwqpwhoffvnyZTVs2FBPPvmkevToUWBMp06dNG/ePPNjFxcXi+39+vVTfHy81qxZo8zMTA0aNEhDhgzRwoULCyVnAMCdITU11a5xAIDS5R9fI/VPPPjgg3rwwQdvGuPi4qLAwMACt+3fv18rV67Utm3b1LRpU0nSRx99pM6dO2vq1KmqWLGi3XMGANwZypSx7legtXEAgNLlln47nDp1Sj/88INOnDihjIwMi23vv/++XRLLtX79evn7+6ts2bK699579eabb6pcuXKSpJiYGPn6+pqLKOnaTIIODg769ddf1b179wL3efXqVV29etX8mAuJAaD08fHx0V9//WVVHAAA17O5kFq7dq0eeugh1ahRQwcOHFD9+vUVFxcnwzDUuHFjuybXqVMn9ejRQ8HBwYqNjdWrr76qBx98UDExMXJ0dFRCQoL8/f0tnlOmTBn5+fkpISHhhvudMmWKJk2aZNdcAQC3lytXrtg1DgBQuti8IO+4ceM0duxY7d69W66urlqyZIlOnjypdu3a6ZFHHrFrco899pgeeughNWjQQJGRkVq2bJm2bdum9evX/6P9jhs3TsnJyebbyZMn7ZMwAOC2Ye3SHSzxAQAoiM2F1P79+9W/f39J13p/0tPT5enpqcmTJ+vtt9+2e4J51ahRQ+XLl9eRI0ckSYGBgTp37pxFTFZWlpKSkm54XZV07borb29vixsAoHSpUKGCXeMAAKWLzYWUh4eH+bqooKAgxcbGmrclJibaL7MCnDp1ShcuXFBQUJAkqVWrVrp48aJ27Nhhjlm3bp1ycnLUokWLQs0FAHB7q1Klil3jAACli83XSLVs2VKbNm1SaGioOnfurBdeeEG7d+9WVFSUWrZsadO+UlNTzb1L0rVp1nfu3Ck/Pz/5+flp0qRJ6tmzpwIDAxUbG6uXXnpJISEh6tixoyQpNDRUnTp10tNPP605c+YoMzNTI0eO1GOPPcaMfQCAm2JoHwDgn7C5kHr//ffNa2pMmjRJqamp+vrrr1WrVi2bZ+zbvn272rdvb348ZswYSdKAAQM0e/Zs7dq1SwsWLNDFixdVsWJFPfDAA3rjjTcs1pL68ssvNXLkSN13333mBXk//PBDW18WAKCUyTuiwh5xAIDSxWQYhlHcSRS3lJQU+fj4KDk5meulAKCUaNasmbZv3/63cU2bNtW2bduKICMAQElgbW1wy6sMbt++Xfv375ck1a1bV02aNLnVXQEAUOQ8PT3tGgcAKF1sLqROnTqlPn36aPPmzfL19ZUkXbx4UWFhYVq0aJEqV65s7xwBALC7tLQ08/2zZ89q6NChio2NVc2aNfXxxx8rICAgXxwAALlsnrVv8ODByszM1P79+5WUlKSkpCTt379fOTk5Gjx4cGHkCACA3Z04ccJ8PyAgQEuXLtXu3bu1dOlScxF1fRwAALls7pHasGGDtmzZotq1a5vbateurY8++kjh4eF2TQ4AgMLi6upq1zgAQOlic49UlSpVlJmZma89OzubKccBALeNJ5980q5xAIDSxeZC6t1339WoUaMsZjravn27nnvuOU2dOtWuyQEAUFjKlLEclNGhQwf9+9//VocOHW4aBwCAdAvTn5ctW1ZpaWnKysoy/3LJve/h4WERm5SUZL9MCxHTnwNA6dOmTRtt3rz5b+Nat26tTZs2FUFGAICSoNCmP//ggw/+SV4AAJQo7dq106ZNm5SdnW1uc3R0VOvWrbVx48ZizAwAUJLZXEgNGDCgMPIAAKBIPfzww9q8ebN+++03VaxYUSdPnjRvq1ixonkR3ocffri4UgQAlGA2D+27EzG0DwBKn4yMDLm4uPxt3NWrV+Xs7FwEGQEASgJrawObJ5sAAOBO4OjoKHd395vGuLu7y9HRsYgyAgDcTiikAACl0vr165WWlqZKlSrJwcHy16Gjo6MqVaqktLQ0rV+/vngSBACUaBRSAIBSKbdAevrpp1W5cmWLbZUqVdLgwYMt4gAAyOuWF8c4cuSIYmNj1bZtW7m5uckwDJlMJnvmBgBAoZs4caK6du2ql19+WW5ubkpPT9eKFSs0adKk4k4NAFCC2VxIXbhwQb1799a6detkMpl0+PBh1ahRQ0899ZTKli2r9957rzDyBADArsLDwyVJnp6e2rNnj5YtW2beVr16dXl6eio1NdUcBwBAXjYP7Rs9erTKlCmjEydOWFyk27t3b61cudKuyQEAUFhyJ5FITU1Venq65s6dqzNnzmju3LlKT09XamqqRRwAAHnZ3CO1evVqrVq1Kt948lq1aun48eN2SwwAgMKUkJBgvp+cnKwhQ4aYH7u5uRUYBwBALpt7pC5fvlzgdLFJSUlWrccBAEBJcP78eUlSx44dlZmZabEtIyND999/v0UcAAB52dwjFR4ers8++0xvvPGGJMlkMiknJ0fvvPOO2rdvb/cEAQAoDBUqVJAkrVq1Sl26dFHnzp3Nk00sX75cP/30k0UcAAB52VxIvfPOO7rvvvu0fft2ZWRk6KWXXtLevXuVlJSkzZs3F0aOAADYXWBgoPm+yWTSPffco/r162vPnj1asWJFgXEAAOSyuZCqX7++Dh06pBkzZsjLy0upqanq0aOHRowYoaCgoMLIEQCAQhMaGqo///xTYWFh5raqVauqTp06OnDgQDFmBgAoyW5pHSkfHx/961//sncuAAAUmXPnzkmS9u/fn28dxJMnT8owDIs4AADysqqQ2rVrl9U7vPvuu285GQAAikreURS5RVNBjxltAQAoiMm4/rdHARwcHGQymWQYhsW3drlPzduWnZ1dCGkWrpSUFPn4+Cg5OVne3t7FnQ4AoAikp6ebZ6GtUKGCBgwYoBo1aujo0aNasGCBeba+tLQ0i+nQAQB3NmtrA6umPz927JiOHj2qY8eOacmSJQoODtasWbO0c+dO7dy5U7NmzVLNmjW1ZMkSu70AAAAK04wZM8z3mzVrpurVq8vFxUXVq1dXs2bNCowDACCXVT1SeTVv3lwTJ05U586dLdqXL1+u119/XTt27LBrgkWBHikAKH3atGmjzZs3q127dtq8ebOysrLM28qUKaOwsDBt3LhRrVu31qZNm4oxUwBAUbK2NrB5sondu3crODg4X3twcLD27dtn6+4AAChWGzZsUJcuXRQSEqL09HS5ubnpyJEj5nWkAAAoiM2FVGhoqKZMmaJPP/1Uzs7Okq6tAD9lyhSFhobaPUEAAApDt27dtHnzZjk4OGjXrl0WhVOVKlXk4OCgnJwcdevWrRizBACUVDYXUnPmzFG3bt1UuXJl8wx9u3btkslk0o8//mj3BAEAKAyNGzeWJOXk5OjkyZMW2/I+zo0DACAvmwup5s2b6+jRo/ryyy/NCxX27t1bffv2lYeHh90TBACgMFi7PhTrSAEACnJLC/J6eHhoyJAh9s4FAIAik5CQIOna1OdJSUkWy3c4OjqqbNmySkxMNMcBAJCXVdOfAwBwp0lKSpIkhYSEKDU1VdOmTdPIkSM1bdo0paamKiQkxCIOAIC8KKQAAKWSg8O1X4Fbt27Vo48+qhYtWuitt95SixYt9Oijj+rXX3+1iAMAIC9+OwAASqWIiAhJUu3atbV7926FhYXJ29tbYWFh2rNnj2rXrm0RBwBAXjYvyHsnYkFeACh9srOzFRQUpPPnz+vBBx+Um5ubLl68KF9fX6Wnp2vFihXy9/fXmTNn5OjoWNzpAgCKSKEtyCtJFy9e1OLFixUbG6sXX3xRfn5++v333xUQEKBKlSrdctIAABQVR0dHzZkzRz179tSKFSsKjJk9ezZFFACgQDYP7du1a5fuuusuvf3225o6daouXrwoSYqKitK4cePsnR8AAIVm69atkvJfB5X3+ikAAApicyE1ZswYDRw4UIcPH5arq6u5vXPnztq4caNdkwMAoLBkZGRo2rRpCggIUFpamn755RctXLhQv/zyi9LS0hQQEKBp06YpIyOjuFMFAJRANhdS27Zt09ChQ/O1V6pUibU2AAC3jVmzZikrK0tvvvmmXFxcFBERoT59+igiIkIuLi6aPHmysrKyNGvWrOJOFQBQAtlcSLm4uCglJSVf+6FDh1ShQgW7JAUAQGGLjY2VJHXt2rXA7bntuXEAAORlcyH10EMPafLkycrMzJQkmUwmnThxQi+//LJ69uxp9wQBACgMNWvWlCQtW7ZM2dnZWr9+vb766iutX79e2dnZWrZsmUUcAAB52Tz9eXJysnr16qXt27fr0qVLqlixohISEtSqVSstX75cHh4ehZVroWH6cwAofTIyMuTh4SEPDw/5+PjoxIkT5m1Vq1ZVcnKyLl++rMuXL8vZ2bkYMwUAFKVCm/7cx8dHa9as0aZNm7Rr1y6lpqaqcePG6tChwz9KGACAouTs7KwuXbro+++/V3p6uvr06aOmTZtq+/btWrJkiTIyMvTwww9TRAEACsSCvKJHCgBKo+zsbIWEhCg9PV1nz57Ntz0gIEDu7u46fPgwa0kBQCli1x6pDz/80OoDP/vss1bHAgBQXKKjoxUXFydJevDBB3X58mUlJiaqfPny8vDwMC/SGx0drYiIiOJLFABQIllVSE2bNs3i8fnz55WWliZfX19J0sWLF+Xu7i5/f38KKQDAbeH06dOSpODgYK1Zs0ZZWVnmbWXKlFFwcLCOHTtmjgMAIC+rZu07duyY+fbvf/9bjRo10v79+5WUlKSkpCTt379fjRs31htvvFHY+QIAYBfnz5+XdO13XLly5fTJJ58oPj5en3zyicqVK6djx45ZxAEAkJfNk028/vrrWrx4sWrXrm1uq127tqZNm6ZevXqpX79+dk0QAIDCULZsWUnXJp04ceKEeVKJwYMHq3///vLy8lJGRoY5DgCAvGxeRyo+Pt5i+EOu7OzsAi/WBQCgJNq2bZuka9Og9+rVSzExMbp06ZJiYmLUq1cvZWRkWMQBAJCXzYXUfffdp6FDh+r33383t+3YsUPDhg1jCnQAwG0jd9Lau+++W7t27VJYWJi8vb0VFham3bt36+6777aIAwAgL5sLqf/9738KDAxU06ZN5eLiIhcXFzVv3lwBAQH69NNPCyNHAADsrlatWpKkXbt26dy5cxbbzp49q127dlnEAQCQ1y2vI3Xo0CEdOHBAklSnTh3ddddddk2sKLGOFACUPhkZGXJzc1NOTo7c3NyUnp5u3pb72MHBQenp6SzKCwCliF3XkSrIXXfddVsXTwCA0s3R0VFeXl5KTk6Wl5eXhg0bpho1aujo0aP64osvlJ6eLi8vLxbjBQAU6JYKqVOnTumHH37QiRMnzBfj5nr//fftkhgAAIUpOjpaycnJ6tevnxYtWmTx+8vR0VF9+/bVwoULWZAXAFAgmwuptWvX6qGHHlKNGjV04MAB1a9fX3FxcTIMQ40bNy6MHAEAsLv4+HhJ0oMPPqhNmzbp+PHj5m2VK1dW586dtXDhQnMcAAB52VxIjRs3TmPHjtWkSZPk5eWlJUuWyN/fX/369VOnTp0KI0cAAOwuKChIkvT444+ra9eueumll8zXRq1YsUKPP/64RRwAAHnZPNmEl5eXdu7cqZo1a6ps2bLatGmT6tWrpz///FMPP/yw4uLiCinVwsNkEwBQ+mRkZMjDw0MeHh7y8fHRiRMnzNuqVq2q5ORkXb58WZcvX2ayCQAoRaytDWye/tzDw8N8XVRQUJBiY2PN2xITE28hVQAAit6WLVuUlZWl5ORkiyJKkk6cOKHk5GRlZWVpy5YtxZQhAKAks3loX8uWLbVp0yaFhoaqc+fOeuGFF7R7925FRUWpZcuWhZEjAAB2d/r0abvGAQBKF5sLqffff1+pqamSpEmTJik1NVVff/21atWqxYx9AIDbRt5eqNTUVG3btk3x8fEKCgpSs2bN5OnpmS8OAIBcNhdSNWrUMN/38PDQnDlz7JoQAABFYfHixZKuDVN3dHTUzp07FRsbq5o1a6ply5YKDAxUQkKCFi9erHHjxhVztgCAkuaWF+QFAOB29tdff0m6Ng26u7u78s69NGbMGPPj3DgAAPKyarKJsmXLys/Pz6qbLTZu3Khu3bqpYsWKMplMWrp0qcV2wzA0fvx4BQUFyc3NTR06dNDhw4ctYpKSktSvXz95e3vL19dXTz31lHnoIQAAN9KgQYMbbjOZTFbFAQBKL6t6pD744APz/QsXLujNN99Ux44d1apVK0lSTEyMVq1apddff92mg1++fFkNGzbUk08+qR49euTb/s477+jDDz/UggULFBwcrNdff10dO3bUvn375OrqKknq16+f4uPjtWbNGmVmZmrQoEEaMmSIFi5caFMuAIDSZd68eSpXrpwk6fqVQHJyciziAAC4ns3rSPXs2VPt27fXyJEjLdpnzJihn3/+OV+vktWJmEz67rvvFBkZKenaL7WKFSvqhRde0NixYyVJycnJCggI0Pz58/XYY49p//79qlu3rrZt26amTZtKklauXKnOnTvr1KlTqlixYoHHunr1qq5evWp+nJKSoipVqrCOFACUIh988IFGjx79t3HTpk3T888/X/gJAQBKhEJbR2rVqlXq1KlTvvZOnTrp559/tnV3N3Ts2DElJCSoQ4cO5jYfHx+1aNFCMTExkq71hPn6+pqLKEnq0KGDHBwc9Ouvv95w31OmTJGPj4/5VqVKFbvlDQC4PRw6dMiucQCA0sXmQqpcuXL6/vvv87V///335iES9pCQkCBJCggIsGgPCAgwb0tISJC/v7/F9jJlysjPz88cU5Bx48YpOTnZfDt58qTd8gYA3B5OnTolSeZpzq/n4eFhEQcAQF42z9o3adIkDR48WOvXr1eLFi0kSb/++qtWrlypTz75xO4JFgYXFxe5uLgUdxoAgGKUO6HEjSYounz5skUcAAB52VxIDRw4UKGhofrwww8VFRUlSQoNDdWmTZvMhZU9BAYGSpLOnj2roKAgc/vZs2fVqFEjc8y5c+csnpeVlaWkpCTz8wEAKEjenihnZ2fVq1dPbm5uSk9P1969e5WRkZEvDgCAXLe0jlSLFi305Zdf2jsXC8HBwQoMDNTatWvNhVNKSop+/fVXDRs2TJLUqlUrXbx4UTt27FCTJk0kSevWrVNOTo5dizoAwJ3H3d3dfD8jI0N//PHH38YBAJDLqkIqJSXFPGNFSkrKTWNtmfUuNTVVR44cMT8+duyYdu7cKT8/P1WtWlXPP/+83nzzTdWqVcs8/XnFihXNM/uFhoaqU6dOevrppzVnzhxlZmZq5MiReuyxx244Yx8AAJJ0/vx5i8cVKlSQp6enUlNTLbZdHwcAgGRlIVW2bFnFx8fL399fvr6+BY4XNwxDJpNJ2dnZVh98+/btat++vfnxmDFjJEkDBgzQ/Pnz9dJLL+ny5csaMmSILl68qDZt2mjlypXmNaQk6csvv9TIkSN13333ycHBQT179tSHH35odQ4AgNLJzc3N4vH58+cLLJqujwMAQLKykFq3bp38/PwkSb/88ovdDh4REZFvEcS8TCaTJk+erMmTJ98wxs/Pj8V3AQA2y51Mwl5xAIDSxapCql27dub7wcHBqlKlSr5eKcMwmEYcAHDbSEtLs2scAKB0sXkdqeDg4AKHPiQlJSk4ONguSQEAUNiuXLli1zgAQOlicyGVey3U9VJTUy2uXQIAoCSrV6+eXeMAAKWL1dOf504EYTKZ9Prrr1tMB5udna1ff/3VPE05AAAlXd5rdJ2cnFSvXj25u7srLS1Ne/fuVWZmZr44AAByWV1I5a6vYRiGdu/eLWdnZ/M2Z2dnNWzYUGPHjrV/hgAAFILY2Fjz/czMTO3cufNv4wAAyGV1IZU7W9+gQYM0ffp0m9aLAgCgpLl+XcTy5cvL0dFR2dnZSkxMvGEcAACSDYVUrnnz5hVGHgAAFKl77rlH27dvNz/OWzxdHwcAwPVsLqQuX76s//znP1q7dq3OnTunnJwci+1Hjx61W3IAABSWmjVr2jUOAFC62FxIDR48WBs2bNATTzyhoKCgAmfwAwCgpIuLi7NrHACgdLG5kFqxYoV++ukntW7dujDyAQCgSFBIAQD+CZvXkSpbtqz8/PwKIxcAAIrM1q1b7RoHAChdbC6k3njjDY0fP15paWmFkQ8AAEXiypUrdo0DAJQuNg/te++99xQbG6uAgABVr15dTk5OFtt///13uyUHAEBhcXCw7rtEa+MAAKWLzYVUZGRkIaQBAEDRun7W2X8aBwAoXUyGYRjFnURxS0lJkY+Pj5KTk1loGABKCVtmneVXJQCUHtbWBoxXAAAAAAAb2Ty0Lzs7W9OmTdM333yjEydOKCMjw2J7UlKS3ZIDAAAAgJLI5h6pSZMm6f3331fv3r2VnJysMWPGqEePHnJwcNDEiRMLIUUAAAAAKFlsLqS+/PJLffLJJ3rhhRdUpkwZ9enTR59++qnGjx/PWhsAAAAASgWbC6mEhAQ1aNBAkuTp6ank5GRJUteuXfXTTz/ZNzsAAAAAKIFsLqQqV66s+Ph4SVLNmjW1evVqSdK2bdvk4uJi3+wAAAAAoASyuZDq3r271q5dK0kaNWqUXn/9ddWqVUv9+/fXk08+afcEAQAAAKCk+cfrSMXExCgmJka1atVSt27d7JVXkWIdKQAofVhHCgBQEGtrA5unP79eq1at1KpVq3+6GwAAAAC4bdhcSH322Wc33d6/f/9bTgYAAAAAbgc2D+0rW7asxePMzEylpaXJ2dlZ7u7ut+WCvAztA4DSh6F9AICCWFsb2DzZxF9//WVxS01N1cGDB9WmTRt99dVX/yhpAAAAALgd2FxIFaRWrVr6z3/+o+eee84euwMAAACAEs0uhZQklSlTRmfOnLHX7gAAAACgxLJ5sokffvjB4rFhGIqPj9eMGTPUunVruyUGAAAAACWVzYVUZGSkxWOTyaQKFSro3nvv1XvvvWevvAAAAACgxLK5kMrJySmMPAAAAADgtnHL10glJiYqJSXFnrkAAAAAwG3BpkLq4sWLGjFihMqXL6+AgACVLVtWgYGBGjdunNLS0gorRwAAAAAoUawe2peUlKRWrVrp9OnT6tevn0JDQyVJ+/bt00cffaQ1a9Zo06ZN2rVrl7Zu3apnn3220JIGAAAAgOJkdSE1efJkOTs7KzY2VgEBAfm2PfDAA3riiSe0evVqffjhh3ZPFAAAAABKCqsLqaVLl+rjjz/OV0RJUmBgoN555x117txZEyZM0IABA+yaJAAAAACUJFZfIxUfH6969erdcHv9+vXl4OCgCRMm2CUxAAAAACiprC6kypcvr7i4uBtuP3bsmPz9/e2REwAAAACUaFYXUh07dtS//vUvZWRk5Nt29epVvf766+rUqZNdkwMAAACAkshkGIZhTeCpU6fUtGlTubi4aMSIEapTp44Mw9D+/fs1a9YsXb16Vdu2bVPVqlULO2e7S0lJkY+Pj5KTk+Xt7V3c6QAAioDJZLI61spflQCAO4C1tYHVk01UrlxZMTExGj58uMaNG2f+pWIymXT//fdrxowZt2URBQAAAAC2srqQkqTg4GCtWLFCf/31lw4fPixJCgkJkZ+fX6EkBwAAAAAlkU2FVK6yZcuqefPm9s4FAAAAAG4LVk82AQAAAAC4hkIKAAAAAGxEIQUAAAAANqKQAgAAAAAbUUgBAAAAgI0opAAAAADARhRSAAAAAGAjCikAAAAAsBGFFAAAAADYiEIKAAAAAGxEIQUAAAAANqKQAgAAAAAblSnuBAAAuF5aZpoOJB4o7jTMfo//vVD3X6d8Hbk7uRfqMQAA9kUhBQAocQ4kHlCTuU2KOw2zws5lx5AdahzUuFCPAQCwLwopAECJU6d8He0YsqNQj9FkovXFUWHnUqd8nULdPwDA/iikAAAljruTe6H30BiGIZPJZFUcAADXY7IJAECp9XdFEkUUAOBGSnQhNXHiRJlMJotbnTr/N/zhypUrGjFihMqVKydPT0/17NlTZ8+eLcaMAQC3mxsVSxRRAICbKdGFlCTVq1dP8fHx5tumTZvM20aPHq0ff/xR3377rTZs2KAzZ86oR48exZgtAOB2ZBiGdpzZIU2UdpzZQREFAPhbJf4aqTJlyigwMDBfe3Jysv773/9q4cKFuvfeeyVJ8+bNU2hoqLZu3aqWLVsWdaoAAAAASokS3yN1+PBhVaxYUTVq1FC/fv104sQJSdKOHTuUmZmpDh06mGPr1KmjqlWrKiYm5qb7vHr1qlJSUixuAAAAAGCtEl1ItWjRQvPnz9fKlSs1e/ZsHTt2TOHh4bp06ZISEhLk7OwsX19fi+cEBAQoISHhpvudMmWKfHx8zLcqVaoU4qsAAAAAcKcp0UP7HnzwQfP9u+++Wy1atFC1atX0zTffyM3N7Zb3O27cOI0ZM8b8OCUlhWIKAAAAgNVKdI/U9Xx9fXXXXXfpyJEjCgwMVEZGhi5evGgRc/bs2QKvqcrLxcVF3t7eFjcAAAAAsNZtVUilpqYqNjZWQUFBatKkiZycnLR27Vrz9oMHD+rEiRNq1apVMWYJAAAA4E5Xoof2jR07Vt26dVO1atV05swZTZgwQY6OjurTp498fHz01FNPacyYMfLz85O3t7dGjRqlVq1aMWMfAAAAgEJVogupU6dOqU+fPrpw4YIqVKigNm3aaOvWrapQoYIkadq0aXJwcFDPnj119epVdezYUbNmzSrmrAEAAADc6Up0IbVo0aKbbnd1ddXMmTM1c+bMIsoIAAAAAG6za6QAAAAAoCSgkAIAAAAAG1FIAQAAAICNKKQAAAAAwEYUUgAAAABgIwopAAAAALARhRQAAAAA2IhCCgAAAABsRCEFAAAAADaikAIAAAAAG1FIAQAAAICNKKQAAAAAwEZlijsBAEDJd/jCYV3KuFTcaRSq/Yn7Lf69k3k5e6lWuVrFnQYA3NYopAAAN3X4wmHdNeOu4k6jyDwe9Xhxp1AkDo08RDEFAP8AhRQA4KZye6K+6P6FQiuEFnM2hSc9K11xF+NU3be63Mq4FXc6hWb/+f16/LvH7/geRgAobBRSAACrhFYIVeOgxsWdRqFqXaV1cacAALhNMNkEAAAAANiIQgoAAAAAbEQhBQAAAAA2opACAAAAABtRSAEAAACAjSikAAAAAMBGTH8OALDK/sT9xZ0C7ICfIwDYB4UUAMAqj0c9XtwpAABQYlBIAQCs8kWPLxRaPrS408A/tD9xP0UxANgBhRQAwCqh5UPVOKhxcacBAECJwGQTAAAAAGAjCikAAAAAsBGFFAAAAADYiEIKAAAAAGxEIQUAAAAANmLWPgDATaVlpkmSfo//vZgzKVzpWemKuxin6r7V5VbGrbjTKTT7z7MgLwDYA4UUAOCmDiQekCQ9/ePTxZwJ7MnL2au4UwCA2xqFFADgpiLrREqS6pSvI3cn9+JNphDlLlRbGhYe9nL2Uq1ytYo7DQC4rVFIAQBuqrx7eQ1uPLi40ygyLDwMALAGk00AAAAAgI0opAAAAADARhRSAAAAAGAjCikAAAAAsBGFFAAAAADYiEIKAAAAAGxEIQUAAAAANqKQAgAAAAAbUUgBAAAAgI0opAAAAADARhRSAAAAAGAjCikAAAAAsBGFFAAAAADYiEIKAAAAAGxEIQUAAAAANqKQAgAAAAAbUUgBAAAAgI0opAAAAADARhRSAAAAAGAjCikAAAAAsBGFFAAAAADYiEIKAFDqZWdna/uW7dJuafuW7crOzi7ulAAAJRyFFACgVIuKilJISIiG9hoqLZGG9hqqkJAQRUVFFXdqAIASrExxJwAAwPXSMtN0IPFAoR9n3fJ1eunplxTeIVwD3xioifsmamLdiVr32Tr16tVL73zyju7tfG+h51GnfB25O7kX+nEAAPZjMgzDKO4kiltKSop8fHyUnJwsb2/v4k4HAEq93+N/V5O5TQr3IDmSPpTkL+kxWY7RyJG0SNI5Sc+q0Mdv7BiyQ42DGhfuQQAAVrG2NqBHCgBQ4tQpX0c7huwo1GNs37JdQy8O1fzP56tBkwZKz0pX3MU4VfetLrcybtrVdJcGPTRIH9/9sZqGNS3UXOqUr1Oo+wcA2N8dU0jNnDlT7777rhISEtSwYUN99NFHat68eXGnBQC4Be5O7oXeQ3Mw46AkqWdET3l6ekqSWldpbd5eK6KWBmmQvDK86C0CAORzR0w28fXXX2vMmDGaMGGCfv/9dzVs2FAdO3bUuXPnijs1AEAJFRQUJEnas2dPgdtz23PjAADI6464RqpFixZq1qyZZsyYIUnKyclRlSpVNGrUKL3yyit/+3yukQKA0ic7O1shISFq0KCBli5dKgeH//tuMScnR5GRkdqzZ48OHz4sR0fHYswUAFCUrK0NbvseqYyMDO3YsUMdOnQwtzk4OKhDhw6KiYkp8DlXr15VSkqKxQ0AULo4Ojrqvffe07JlyxQZGamYmBhdunRJMTExioyM1LJlyzR16lSKKABAgW77QioxMVHZ2dkKCAiwaA8ICFBCQkKBz5kyZYp8fHzMtypVqhRFqgCAEqZHjx5avHixdu/erbCwMHl7eyssLEx79uzR4sWL1aNHj+JOEQBQQt0xk03YYty4cRozZoz5cUpKCsUUAJRSPXr00MMPP6zo6GjFx8crKChI4eHh9EQBAG7qti+kypcvL0dHR509e9ai/ezZswoMDCzwOS4uLnJxcSmK9AAAtwFHR0dFREQUdxoAgNvIbT+0z9nZWU2aNNHatWvNbTk5OVq7dq1atWpVjJkBAAAAuFPd9j1SkjRmzBgNGDBATZs2VfPmzfXBBx/o8uXLGjRoUHGnBgAAAOAOdEcUUr1799b58+c1fvx4JSQkqFGjRlq5cmW+CSgAAAAAwB7uiHWk/inWkQIAAAAglaJ1pAAAAACgqFFIAQAAAICNKKQAAAAAwEYUUgAAAABgIwopAAAAALDRHTH9+T+VO3FhSkpKMWcCAAAAoDjl1gR/N7k5hZSkS5cuSZKqVKlSzJkAAAAAKAkuXbokHx+fG25nHSlJOTk5OnPmjLy8vGQymYo7HQBAMUhJSVGVKlV08uRJ1hQEgFLMMAxdunRJFStWlIPDja+EopACAEAszg4AsA2TTQAAAACAjSikAAAAAMBGFFIAAEhycXHRhAkT5OLiUtypAABuA1wjBQAAAAA2okcKAAAAAGxEIQUAAAAANqKQAgAAAAAbUUgBAAAAgI0opAAAxW7gwIGKjIws7jRuav78+fL19bUqduLEiWrUqFGh5mMLk8mkpUuXFncaAHBHoZACABQoIiJCzz//fKE/B/ZT0go4ALiTUUgBAJBHRkZGcacAALgNUEgBAPIZOHCgNmzYoOnTp8tkMslkMikuLk4bNmxQ8+bN5eLioqCgIL3yyivKysq66XOys7P11FNPKTg4WG5ubqpdu7amT59+S3ktW7ZMvr6+ys7OliTt3LlTJpNJr7zyijlm8ODBevzxx82PlyxZonr16snFxUXVq1fXe++9Z7HP6tWr64033lD//v3l7e2tIUOGSLo2lK9q1apyd3dX9+7ddeHChVvKOdenn36q0NBQubq6qk6dOpo1a5Z5W1xcnEwmk6KiotS+fXu5u7urYcOGiomJsdjHJ598oipVqphzev/9983DDefPn69Jkybpzz//NJ//+fPnm5+bmJio7t27y93dXbVq1dIPP/zwj14PAJR6BgAA17l48aLRqlUr4+mnnzbi4+ON+Ph449SpU4a7u7sxfPhwY//+/cZ3331nlC9f3pgwYcINn5OVlWVkZGQY48ePN7Zt22YcPXrU+OKLLwx3d3fj66+/Nh9vwIABxsMPP2xVXg4ODsa2bdsMwzCMDz74wChfvrzRokULc0xISIjxySefGIZhGNu3bzccHByMyZMnGwcPHjTmzZtnuLm5GfPmzTPHV6tWzfD29jamTp1qHDlyxDhy5IixdetWw8HBwXj77beNgwcPGtOnTzd8fX0NHx8fq87fhAkTjIYNG5off/HFF0ZQUJCxZMkS4+jRo8aSJUsMPz8/Y/78+YZhGMaxY8cMSUadOnWMZcuWGQcPHjR69eplVKtWzcjMzDQMwzA2bdpkODg4GO+++65x8OBBY+bMmYafn585p7S0NOOFF14w6tWrZz7/aWlphmEYhiSjcuXKxsKFC43Dhw8bzz77rOHp6WlcuHDBqtcDAMiPQgoAUKB27doZzz33nPnxq6++atSuXdvIyckxt82cOdPw9PQ0srOzC3zOjYwYMcLo2bOn+bG1hZRhGEbjxo2Nd9991zAMw4iMjDT+/e9/G87OzsalS5eMU6dOGZKMQ4cOGYZhGH379jXuv/9+i+e/+OKLRt26dc2Pq1WrZkRGRlrE9OnTx+jcubNFW+/evW+5kKpZs6axcOFCi5g33njDaNWqlWEY/1dIffrpp+bte/fuNSQZ+/fvNx+/S5cuFvvo16+fRU7XHzeXJOO1114zP05NTTUkGStWrLDq9QAA8mNoHwDAKvv371erVq1kMpnMba1bt1ZqaqpOnTp10+fOnDlTTZo0UYUKFeTp6am5c+fqxIkTt5RHu3bttH79ehmGoejoaPXo0UOhoaHatGmTNmzYoIoVK6pWrVrmnFu3bm3x/NatW+vw4cPm4YGS1LRp03yvtUWLFhZtrVq1uqV8L1++rNjYWD311FPy9PQ03958803FxsZaxN59993m+0FBQZKkc+fOSZIOHjyo5s2bW8Rf//hm8u7bw8ND3t7e5n0DAGxXprgTAADc2RYtWqSxY8fqvffeU6tWreTl5aV3331Xv/766y3tLyIiQv/73//0559/ysnJSXXq1FFERITWr1+vv/76S+3atbN5nx4eHreUizVSU1MlXbu+6frizNHR0eKxk5OT+X5uwZqTk2OXPPLuO3f/9to3AJRGFFIAgAI5Oztb9NqEhoZqyZIlMgzD/Ef+5s2b5eXlpcqVKxf4nNyYsLAwDR8+3Nx2fU+MLcLDw3Xp0iVNmzbNXDRFREToP//5j/766y+98MILFjlv3rw5Xz533XVXviImr9DQ0HyF3tatW28p34CAAFWsWFFHjx5Vv379bmkfklS7dm1t27bNou36xwWdfwBA4WBoHwCgQNWrV9evv/6quLg4JSYmavjw4Tp58qRGjRqlAwcO6Pvvv9eECRM0ZswYOTg4FPicnJwc1apVS9u3b9eqVat06NAhvf766/kKAFuULVtWd999t7788ktFRERIktq2bavff/9dhw4dsuiReuGFF7R27Vq98cYbOnTokBYsWKAZM2Zo7NixNz3Gs88+q5UrV2rq1Kk6fPiwZsyYoZUrV95yzpMmTdKUKVP04Ycf6tChQ9q9e7fmzZun999/3+p9jBo1SsuXL9f777+vw4cP6+OPP9aKFSsshlpWr15dx44d086dO5WYmKirV6/ecs4AgJujkAIAFGjs2LFydHRU3bp1VaFCBWVmZmr58uX67bff1LBhQz3zzDN66qmn9Nprr93wOSdOnNDQoUPVo0cP9e7dWy1atNCFCxcseqduRbt27ZSdnW0upPz8/FS3bl0FBgaqdu3a5rjGjRvrm2++0aJFi1S/fn2NHz9ekydP1sCBA2+6/5YtW+qTTz7R9OnT1bBhQ61evdriddpq8ODB+vTTTzVv3jw1aNBA7dq10/z58xUcHGz1Plq3bq05c+bo/fffV8OGDbVy5UqNHj1arq6u5piePXuqU6dOat++vSpUqKCvvvrqlnMGANycyTAMo7iTAAAAtnv66ad14MABRUdHF3cqAFDqcI0UAAC3ialTp+r++++Xh4eHVqxYoQULFlgs7AsAKDr0SAEASowTJ06obt26N9y+b98+Va1atQgzKli9evV0/PjxArd9/PHH/2hSiZt59NFHtX79el26dEk1atTQqFGj9MwzzxTKsQAAN0chBQAoMbKyshQXF3fD7dWrV1eZMsU/mOL48ePKzMwscFtAQIC8vLyKOCMAQFGjkAIAAAAAGzFrHwAAAADYiEIKAAAAAGxEIQUAAAAANqKQAgAAAAAbUUgBAAAAgI0opAAAAADARhRSAAAAAGCj/wfTp3Fu4eX67wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot da quantidade de palavras em cada notícia\n",
    "df_word_length['total_word_length'].plot(kind='box', figsize=(10, 5), color='green')\n",
    "plt.title('Boxplot da quantidade de palavras em cada notícia')\n",
    "plt.ylabel('Quantidade de palavras')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1 - 1.5*IQR: 3.5\n",
      "Quantidade de notícias com quantidade de palavras total menor que 1.5*IQR: 1\n"
     ]
    }
   ],
   "source": [
    "# Removendo as noticias com quantidade de palavras total menor que 1.5*IQR\n",
    "\n",
    "Q1 = df_word_length['total_word_length'].quantile(0.25)\n",
    "Q3 = df_word_length['total_word_length'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Calculando a quantidade de palavra total menor que 1.5*IQR\n",
    "print('Q1 - 1.5*IQR:', Q1 - 1.5*IQR)\n",
    "print('Quantidade de notícias com quantidade de palavras total menor que 1.5*IQR:', df_word_length[df_word_length['total_word_length'] < (Q1 - 1.5 * IQR)]['total_word_length'].count())\n",
    "\n",
    "# Removendo as noticias com quantidade de palavras total menor que 1.5*IQR\n",
    "df = df_word_length[df_word_length['total_word_length'] > (Q1 - 1.5 * IQR)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "link                       84219\n",
       "headline                   84219\n",
       "category                   84219\n",
       "short_description          84219\n",
       "authors                    84219\n",
       "date                       84219\n",
       "word_length_headline       84219\n",
       "word_length_description    84219\n",
       "total_word_length          84219\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tratamento em nível de tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenização"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'For the past 18 months, Hollywood has effectively boycotted the Globes after reports that the HFPA’s 87 members of non-American journalists included no Black members.'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw = df['short_description'].iloc[0]\n",
    "raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['For',\n",
       " 'the',\n",
       " 'past',\n",
       " '18',\n",
       " 'months',\n",
       " ',',\n",
       " 'Hollywood',\n",
       " 'has',\n",
       " 'effectively',\n",
       " 'boycotted',\n",
       " 'the',\n",
       " 'Globes',\n",
       " 'after',\n",
       " 'reports',\n",
       " 'that',\n",
       " 'the',\n",
       " 'HFPA',\n",
       " '’',\n",
       " 's',\n",
       " '87',\n",
       " 'members',\n",
       " 'of',\n",
       " 'non-American',\n",
       " 'journalists',\n",
       " 'included',\n",
       " 'no',\n",
       " 'Black',\n",
       " 'members',\n",
       " '.']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens = word_tokenize(raw, preserve_line=True)\n",
    "tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Minúsculo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['for',\n",
       " 'the',\n",
       " 'past',\n",
       " '18',\n",
       " 'months',\n",
       " ',',\n",
       " 'hollywood',\n",
       " 'has',\n",
       " 'effectively',\n",
       " 'boycotted',\n",
       " 'the',\n",
       " 'globes',\n",
       " 'after',\n",
       " 'reports',\n",
       " 'that',\n",
       " 'the',\n",
       " 'hfpa',\n",
       " '’',\n",
       " 's',\n",
       " '87',\n",
       " 'members',\n",
       " 'of',\n",
       " 'non-american',\n",
       " 'journalists',\n",
       " 'included',\n",
       " 'no',\n",
       " 'black',\n",
       " 'members',\n",
       " '.']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lowered_tokens = [token.lower() for token in tokens]\n",
    "lowered_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remoção de números e sinais de pontuação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['for',\n",
       " 'the',\n",
       " 'past',\n",
       " 'months',\n",
       " 'hollywood',\n",
       " 'has',\n",
       " 'effectively',\n",
       " 'boycotted',\n",
       " 'the',\n",
       " 'globes',\n",
       " 'after',\n",
       " 'reports',\n",
       " 'that',\n",
       " 'the',\n",
       " 'hfpa',\n",
       " 's',\n",
       " 'members',\n",
       " 'of',\n",
       " 'journalists',\n",
       " 'included',\n",
       " 'no',\n",
       " 'black',\n",
       " 'members']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = [token for token in lowered_tokens if token.isalpha()]\n",
    "words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remoção de Stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/manoelsilva/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i',\n",
       " 'me',\n",
       " 'my',\n",
       " 'myself',\n",
       " 'we',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'you',\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " \"you'll\",\n",
       " \"you'd\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves',\n",
       " 'he',\n",
       " 'him',\n",
       " 'his',\n",
       " 'himself',\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'her',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'they',\n",
       " 'them',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'themselves',\n",
       " 'what',\n",
       " 'which',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'this',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'these',\n",
       " 'those',\n",
       " 'am',\n",
       " 'is',\n",
       " 'are',\n",
       " 'was',\n",
       " 'were',\n",
       " 'be',\n",
       " 'been',\n",
       " 'being',\n",
       " 'have',\n",
       " 'has',\n",
       " 'had',\n",
       " 'having',\n",
       " 'do',\n",
       " 'does',\n",
       " 'did',\n",
       " 'doing',\n",
       " 'a',\n",
       " 'an',\n",
       " 'the',\n",
       " 'and',\n",
       " 'but',\n",
       " 'if',\n",
       " 'or',\n",
       " 'because',\n",
       " 'as',\n",
       " 'until',\n",
       " 'while',\n",
       " 'of',\n",
       " 'at',\n",
       " 'by',\n",
       " 'for',\n",
       " 'with',\n",
       " 'about',\n",
       " 'against',\n",
       " 'between',\n",
       " 'into',\n",
       " 'through',\n",
       " 'during',\n",
       " 'before',\n",
       " 'after',\n",
       " 'above',\n",
       " 'below',\n",
       " 'to',\n",
       " 'from',\n",
       " 'up',\n",
       " 'down',\n",
       " 'in',\n",
       " 'out',\n",
       " 'on',\n",
       " 'off',\n",
       " 'over',\n",
       " 'under',\n",
       " 'again',\n",
       " 'further',\n",
       " 'then',\n",
       " 'once',\n",
       " 'here',\n",
       " 'there',\n",
       " 'when',\n",
       " 'where',\n",
       " 'why',\n",
       " 'how',\n",
       " 'all',\n",
       " 'any',\n",
       " 'both',\n",
       " 'each',\n",
       " 'few',\n",
       " 'more',\n",
       " 'most',\n",
       " 'other',\n",
       " 'some',\n",
       " 'such',\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'only',\n",
       " 'own',\n",
       " 'same',\n",
       " 'so',\n",
       " 'than',\n",
       " 'too',\n",
       " 'very',\n",
       " 's',\n",
       " 't',\n",
       " 'can',\n",
       " 'will',\n",
       " 'just',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'now',\n",
       " 'd',\n",
       " 'll',\n",
       " 'm',\n",
       " 'o',\n",
       " 're',\n",
       " 've',\n",
       " 'y',\n",
       " 'ain',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'ma',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\"]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['past',\n",
       " 'months',\n",
       " 'hollywood',\n",
       " 'effectively',\n",
       " 'boycotted',\n",
       " 'globes',\n",
       " 'reports',\n",
       " 'hfpa',\n",
       " 'members',\n",
       " 'journalists',\n",
       " 'included',\n",
       " 'black',\n",
       " 'members']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_stopwords_tokens = [word for word in words if word not in stopwords.words(\"english\")]\n",
    "no_stopwords_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lematização"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     /Users/manoelsilva/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['For',\n",
       " 'the',\n",
       " 'past',\n",
       " '18',\n",
       " 'month',\n",
       " ',',\n",
       " 'Hollywood',\n",
       " 'ha',\n",
       " 'effectively',\n",
       " 'boycotted',\n",
       " 'the',\n",
       " 'Globes',\n",
       " 'after',\n",
       " 'report',\n",
       " 'that',\n",
       " 'the',\n",
       " 'HFPA',\n",
       " '’',\n",
       " 's',\n",
       " '87',\n",
       " 'member',\n",
       " 'of',\n",
       " 'non-American',\n",
       " 'journalist',\n",
       " 'included',\n",
       " 'no',\n",
       " 'Black',\n",
       " 'member',\n",
       " '.']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wnl = nltk.WordNetLemmatizer()\n",
    "\n",
    "lemmatized_tokens = [wnl.lemmatize(token) for token in tokens]\n",
    "lemmatized_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['for',\n",
       " 'the',\n",
       " 'past',\n",
       " '18',\n",
       " 'month',\n",
       " ',',\n",
       " 'hollywood',\n",
       " 'ha',\n",
       " 'effect',\n",
       " 'boycot',\n",
       " 'the',\n",
       " 'globe',\n",
       " 'after',\n",
       " 'report',\n",
       " 'that',\n",
       " 'the',\n",
       " 'hfpa',\n",
       " '’',\n",
       " 's',\n",
       " '87',\n",
       " 'member',\n",
       " 'of',\n",
       " 'non-american',\n",
       " 'journalist',\n",
       " 'includ',\n",
       " 'no',\n",
       " 'black',\n",
       " 'member',\n",
       " '.']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "porter = nltk.PorterStemmer()\n",
    "\n",
    "steamed_tokens = [porter.stem(token) for token in tokens]\n",
    "steamed_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Função de tratamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_text(text):\n",
    "    # Tokenização\n",
    "    tokens = word_tokenize(text, preserve_line=True)\n",
    "\n",
    "    return tokens\n",
    "\n",
    "def lower_tokens(tokens):\n",
    "    # Conversão para minúsculas\n",
    "    tokens = [token.lower() for token in tokens]\n",
    "\n",
    "    return tokens\n",
    "\n",
    "def concatenate_tokens(tokens):\n",
    "    # Concatenando tokens separados por espaços\n",
    "    tokens_as_text = ' '.join(tokens)\n",
    "\n",
    "    return tokens_as_text\n",
    "\n",
    "def remove_not_words(tokens):\n",
    "    # Remoção de tokens que não são palavras (somente caracteres alfabéticos)\n",
    "    words = [token for token in tokens if token.isalpha()]\n",
    "\n",
    "    return words\n",
    "\n",
    "def remove_stop_words(tokens):\n",
    "    # Remoção de stop words\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    filtered_words = [token for token in tokens if token not in stop_words]\n",
    "\n",
    "    return filtered_words\n",
    "\n",
    "def lemmatize_tokens(tokens):\n",
    "    wnl = nltk.WordNetLemmatizer()\n",
    "\n",
    "    lemmatized_tokens = [wnl.lemmatize(token) for token in tokens]\n",
    "\n",
    "    return lemmatized_tokens\n",
    "\n",
    "def stemming_tokens(tokens):\n",
    "    # Stemming\n",
    "    porter = nltk.PorterStemmer()\n",
    "\n",
    "    steamed_tokens = []\n",
    "\n",
    "    for token in tokens:\n",
    "        steamed_token = porter.stem(token)\n",
    "\n",
    "        # Remoção de tokens com menos de 3 caracteres\n",
    "        if len(steamed_token) > 2:\n",
    "            steamed_tokens.append(steamed_token)\n",
    "\n",
    "    return steamed_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_text(text):\n",
    "    tokens = tokenize_text(text)\n",
    "    tokens = remove_not_words(tokens)\n",
    "    tokens = lower_tokens(tokens)\n",
    "    tokens = remove_stop_words(tokens)\n",
    "    tokens = stemming_tokens(tokens)\n",
    "\n",
    "    return concatenate_tokens(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'past month hollywood effect boycot globe report hfpa member journalist includ black member'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "process_text(raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metadados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_words(all_text_tokens):\n",
    "    return sum(len(tokens) for tokens in all_text_tokens)\n",
    "\n",
    "def count_distinct_words(all_text_tokens):\n",
    "    distinct_tokens = set(token for tokens in all_text_tokens for token in tokens)\n",
    "\n",
    "    return len(distinct_tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tratamento de todos os documentos\n",
    "\n",
    "### Etapas\n",
    "\n",
    "1. Tokenização, remoção de números/pontuação e transformação para minúsculo\n",
    "2. Remoção de stopwords\n",
    "3. Aplicação do Stemming\n",
    "\n",
    "### Casos\n",
    "\n",
    "1. Título\n",
    "2. Resumo\n",
    "3. Título + Resumo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_all_texts(df, columns):\n",
    "    all_texts_tokens = []\n",
    "\n",
    "    for index, instance in df.iterrows():\n",
    "        instance_tokens = []\n",
    "\n",
    "        for column in columns:\n",
    "            text = instance[column]\n",
    "            tokens = tokenize_text(text)\n",
    "            tokens = lower_tokens(tokens)\n",
    "\n",
    "            instance_tokens = instance_tokens + tokens\n",
    "\n",
    "        all_texts_tokens.append(instance_tokens)\n",
    "    \n",
    "    return all_texts_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Caso 1 (Título)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tokenização e filtragem de palavras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de tokens: 927048\n",
      "Total de tokens distintos: 44999\n"
     ]
    }
   ],
   "source": [
    "columns = ['headline']\n",
    "\n",
    "all_texts_tokens_1 = tokenize_all_texts(df, columns)\n",
    "\n",
    "total_words = count_words(all_texts_tokens_1)\n",
    "distinct_words = count_distinct_words(all_texts_tokens_1)\n",
    "\n",
    "print(f'Total de tokens: {total_words}')\n",
    "print(f'Total de tokens distintos: {distinct_words}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remoção de Non-Alpha Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de tokens: 781095\n",
      "Total de tokens distintos: 34312\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(all_texts_tokens_1)):\n",
    "    all_texts_tokens_1[i] = remove_not_words(all_texts_tokens_1[i])\n",
    "\n",
    "total_words = count_words(all_texts_tokens_1)\n",
    "distinct_words = count_distinct_words(all_texts_tokens_1)\n",
    "\n",
    "print(f'Total de tokens: {total_words}')\n",
    "print(f'Total de tokens distintos: {distinct_words}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remoção de stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de tokens: 543900\n",
      "Total de tokens distintos: 34164\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(all_texts_tokens_1)):\n",
    "    all_texts_tokens_1[i] = remove_stop_words(all_texts_tokens_1[i])\n",
    "\n",
    "total_words = count_words(all_texts_tokens_1)\n",
    "distinct_words = count_distinct_words(all_texts_tokens_1)\n",
    "\n",
    "print(f'Total de tokens: {total_words}')\n",
    "print(f'Total de tokens distintos: {distinct_words}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de tokens: 537231\n",
      "Total de tokens distintos: 23380\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(all_texts_tokens_1)):\n",
    "    all_texts_tokens_1[i] = stemming_tokens(all_texts_tokens_1[i])\n",
    "\n",
    "total_words = count_words(all_texts_tokens_1)\n",
    "distinct_words = count_distinct_words(all_texts_tokens_1)\n",
    "\n",
    "print(f'Total de tokens: {total_words}')\n",
    "print(f'Total de tokens distintos: {distinct_words}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Caso 2 (Resumo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tokenização e filtragem de palavras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de tokens: 2069858\n",
      "Total de tokens distintos: 68700\n"
     ]
    }
   ],
   "source": [
    "columns = ['short_description']\n",
    "all_texts_tokens_2 = tokenize_all_texts(df, columns)\n",
    "\n",
    "total_words = count_words(all_texts_tokens_2)\n",
    "distinct_words = count_distinct_words(all_texts_tokens_2)\n",
    "\n",
    "print(f'Total de tokens: {total_words}')\n",
    "print(f'Total de tokens distintos: {distinct_words}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remoção de Non-Alpha Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de tokens: 1740982\n",
      "Total de tokens distintos: 47849\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(all_texts_tokens_2)):\n",
    "    all_texts_tokens_2[i] = remove_not_words(all_texts_tokens_2[i])\n",
    "\n",
    "total_words = count_words(all_texts_tokens_2)\n",
    "distinct_words = count_distinct_words(all_texts_tokens_2)\n",
    "\n",
    "print(f'Total de tokens: {total_words}')\n",
    "print(f'Total de tokens distintos: {distinct_words}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remoção de stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de tokens: 935789\n",
      "Total de tokens distintos: 47699\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(all_texts_tokens_2)):\n",
    "    all_texts_tokens_2[i] = remove_stop_words(all_texts_tokens_2[i])\n",
    "\n",
    "total_words = count_words(all_texts_tokens_2)\n",
    "distinct_words = count_distinct_words(all_texts_tokens_2)\n",
    "\n",
    "print(f'Total de tokens: {total_words}')\n",
    "print(f'Total de tokens distintos: {distinct_words}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de tokens: 922907\n",
      "Total de tokens distintos: 31619\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(all_texts_tokens_2)):\n",
    "    all_texts_tokens_2[i] = stemming_tokens(all_texts_tokens_2[i])\n",
    "\n",
    "total_words = count_words(all_texts_tokens_2)\n",
    "distinct_words = count_distinct_words(all_texts_tokens_2)\n",
    "\n",
    "print(f'Total de tokens: {total_words}')\n",
    "print(f'Total de tokens distintos: {distinct_words}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Caso 3 (Título + Resumo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tokenização e filtragem de palavras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de tokens: 2996906\n",
      "Total de tokens distintos: 83152\n"
     ]
    }
   ],
   "source": [
    "columns = ['short_description', 'headline']\n",
    "all_texts_tokens_3 = tokenize_all_texts(df, columns)\n",
    "\n",
    "total_words = count_words(all_texts_tokens_3)\n",
    "distinct_words = count_distinct_words(all_texts_tokens_3)\n",
    "\n",
    "print(f'Total de tokens: {total_words}')\n",
    "print(f'Total de tokens distintos: {distinct_words}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remoção de Non-Alpha Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de tokens: 2522077\n",
      "Total de tokens distintos: 54877\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(all_texts_tokens_3)):\n",
    "    all_texts_tokens_3[i] = remove_not_words(all_texts_tokens_3[i])\n",
    "\n",
    "total_words = count_words(all_texts_tokens_3)\n",
    "distinct_words = count_distinct_words(all_texts_tokens_3)\n",
    "\n",
    "print(f'Total de tokens: {total_words}')\n",
    "print(f'Total de tokens distintos: {distinct_words}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remoção de stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de tokens: 1479689\n",
      "Total de tokens distintos: 54727\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(all_texts_tokens_3)):\n",
    "    all_texts_tokens_3[i] = remove_stop_words(all_texts_tokens_3[i])\n",
    "\n",
    "total_words = count_words(all_texts_tokens_3)\n",
    "distinct_words = count_distinct_words(all_texts_tokens_3)\n",
    "\n",
    "print(f'Total de tokens: {total_words}')\n",
    "print(f'Total de tokens distintos: {distinct_words}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de tokens: 1460138\n",
      "Total de tokens distintos: 36403\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(all_texts_tokens_3)):\n",
    "    all_texts_tokens_3[i] = stemming_tokens(all_texts_tokens_3[i])\n",
    "\n",
    "total_words = count_words(all_texts_tokens_3)\n",
    "distinct_words = count_distinct_words(all_texts_tokens_3)\n",
    "\n",
    "print(f'Total de tokens: {total_words}')\n",
    "print(f'Total de tokens distintos: {distinct_words}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenar todos os tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_texts_tokens_1 = [concatenate_tokens(tokens) for tokens in all_texts_tokens_1]\n",
    "all_texts_tokens_2 = [concatenate_tokens(tokens) for tokens in all_texts_tokens_2]\n",
    "all_texts_tokens_3 = [concatenate_tokens(tokens) for tokens in all_texts_tokens_3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Armazenamento de todos os tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tokens = pd.DataFrame({\n",
    "    'headline': all_texts_tokens_1,\n",
    "    'short_description': all_texts_tokens_2,\n",
    "    'mix': all_texts_tokens_3,\n",
    "    'category': df['category']})\n",
    "\n",
    "df_tokens.to_csv('news_tokens.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vetorização dos documentos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seleção de instâncias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Por frequência"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(84219, 23380)\n",
      "(84219, 31619)\n",
      "(84219, 36403)\n"
     ]
    }
   ],
   "source": [
    "frequency_vectorizer = CountVectorizer()\n",
    "\n",
    "frequency_vecs_1 = frequency_vectorizer.fit_transform(all_texts_tokens_1)\n",
    "frequency_vecs_2 = frequency_vectorizer.fit_transform(all_texts_tokens_2)\n",
    "frequency_vecs_3 = frequency_vectorizer.fit_transform(all_texts_tokens_3)\n",
    "\n",
    "print(frequency_vecs_1.shape)\n",
    "print(frequency_vecs_2.shape)\n",
    "print(frequency_vecs_3.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Armazenando com numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez_compressed('frequency_vectors.npz', \n",
    "        headline=frequency_vecs_1, \n",
    "        short_description=frequency_vecs_2, \n",
    "        mix=frequency_vecs_3, \n",
    "        category=df['category'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Por frequência inversa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(84219, 23380)\n",
      "(84219, 31619)\n",
      "(84219, 36403)\n"
     ]
    }
   ],
   "source": [
    "inverse_frequency_vectorizer = TfidfVectorizer()\n",
    "\n",
    "inverse_frequency_vecs_1 = inverse_frequency_vectorizer.fit_transform(all_texts_tokens_1)\n",
    "inverse_frequency_vecs_2 = inverse_frequency_vectorizer.fit_transform(all_texts_tokens_2)\n",
    "inverse_frequency_vecs_3 = inverse_frequency_vectorizer.fit_transform(all_texts_tokens_3)\n",
    "\n",
    "print(inverse_frequency_vecs_1.shape)\n",
    "print(inverse_frequency_vecs_2.shape)\n",
    "print(inverse_frequency_vecs_3.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Armazenando com numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez_compressed('inverse_frequency_vectors.npz',\n",
    "        headline=inverse_frequency_vecs_1, \n",
    "        short_description=inverse_frequency_vecs_2, \n",
    "        mix=inverse_frequency_vecs_3, \n",
    "        category=df['category'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seleção de Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Formato da Frequencia:\n",
      "(84219, 10000)\n",
      "(84219, 10000)\n",
      "(84219, 10000)\n",
      "Formato da Frequencia Inversa:\n",
      "(84219, 10000)\n",
      "(84219, 10000)\n",
      "(84219, 10000)\n"
     ]
    }
   ],
   "source": [
    "# Cortando a vetorização para 10000 dimensões, ou seja, as 10000 palavras mais frequentes\n",
    "\n",
    "frequency_vectorizer = CountVectorizer(max_features=10000)\n",
    "inverse_frequency_vectorizer = TfidfVectorizer(max_features=10000)\n",
    "\n",
    "frequency_vecs_1 = frequency_vectorizer.fit_transform(all_texts_tokens_1)\n",
    "frequency_vecs_2 = frequency_vectorizer.fit_transform(all_texts_tokens_2)\n",
    "frequency_vecs_3 = frequency_vectorizer.fit_transform(all_texts_tokens_3)\n",
    "\n",
    "inverse_frequency_vecs_1 = inverse_frequency_vectorizer.fit_transform(all_texts_tokens_1)\n",
    "inverse_frequency_vecs_2 = inverse_frequency_vectorizer.fit_transform(all_texts_tokens_2)\n",
    "inverse_frequency_vecs_3 = inverse_frequency_vectorizer.fit_transform(all_texts_tokens_3)\n",
    "\n",
    "print('Formato da Frequencia:')\n",
    "print(frequency_vecs_1.shape)\n",
    "print(frequency_vecs_2.shape)\n",
    "print(frequency_vecs_3.shape)\n",
    "\n",
    "print('Formato da Frequencia Inversa:')\n",
    "print(inverse_frequency_vecs_1.shape)\n",
    "print(inverse_frequency_vecs_2.shape)\n",
    "print(inverse_frequency_vecs_3.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(84219, 1291)\n",
      "(84219, 1911)\n",
      "(84219, 2625)\n",
      "Total de noticias: 84219\n",
      "Noticias sem palavras que aparecem em pelo menos 0.1% das noticias: 3\n"
     ]
    }
   ],
   "source": [
    "# Cortando para as palavras que aparecem em pelo menos 0.1% das notícias\n",
    "inverse_frequency_vectorizer = TfidfVectorizer(min_df=0.001)\n",
    "\n",
    "inverse_frequency_vecs_1 = inverse_frequency_vectorizer.fit_transform(all_texts_tokens_1)\n",
    "inverse_frequency_vecs_2 = inverse_frequency_vectorizer.fit_transform(all_texts_tokens_2)\n",
    "inverse_frequency_vecs_3 = inverse_frequency_vectorizer.fit_transform(all_texts_tokens_3)\n",
    "\n",
    "print(inverse_frequency_vecs_1.shape)\n",
    "print(inverse_frequency_vecs_2.shape)\n",
    "print(inverse_frequency_vecs_3.shape)\n",
    "\n",
    "# Quantas noticias não possuem palavras que aparecem em pelo menos 0.1% das noticias em 'mix'\n",
    "print('Total de noticias:', inverse_frequency_vecs_3.shape[0])\n",
    "print('Noticias sem palavras que aparecem em pelo menos 0.1% das noticias:', np.sum(inverse_frequency_vecs_3.sum(axis=1) == 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(84219, 182)\n",
      "(84219, 435)\n",
      "(84219, 681)\n",
      "Total de noticias: 84219\n",
      "Noticias sem palavras que aparecem em pelo menos 0.1% das noticias: 108\n"
     ]
    }
   ],
   "source": [
    "# Cortando para as palavras que aparecem em pelo menos 0.5% das notícias\n",
    "inverse_frequency_vectorizer = TfidfVectorizer(min_df=0.005)\n",
    "\n",
    "inverse_frequency_vecs_1 = inverse_frequency_vectorizer.fit_transform(all_texts_tokens_1)\n",
    "inverse_frequency_vecs_2 = inverse_frequency_vectorizer.fit_transform(all_texts_tokens_2)\n",
    "inverse_frequency_vecs_3 = inverse_frequency_vectorizer.fit_transform(all_texts_tokens_3)\n",
    "\n",
    "print(inverse_frequency_vecs_1.shape)\n",
    "print(inverse_frequency_vecs_2.shape)\n",
    "print(inverse_frequency_vecs_3.shape)\n",
    "\n",
    "# Quantas noticias não possuem palavras que aparecem em pelo menos 0.1% das noticias em 'mix'\n",
    "print('Total de noticias:', inverse_frequency_vecs_3.shape[0])\n",
    "print('Noticias sem palavras que aparecem em pelo menos 0.1% das noticias:', np.sum(inverse_frequency_vecs_3.sum(axis=1) == 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(84219, 60)\n",
      "(84219, 156)\n",
      "(84219, 263)\n",
      "Total de noticias: 84219\n",
      "Noticias sem palavras que aparecem em pelo menos 0.1% das noticias: 828\n"
     ]
    }
   ],
   "source": [
    "# Cortando para as palavras que aparecem em pelo menos 1% das notícias\n",
    "inverse_frequency_vectorizer = TfidfVectorizer(min_df=0.01)\n",
    "\n",
    "inverse_frequency_vecs_1 = inverse_frequency_vectorizer.fit_transform(all_texts_tokens_1)\n",
    "inverse_frequency_vecs_2 = inverse_frequency_vectorizer.fit_transform(all_texts_tokens_2)\n",
    "inverse_frequency_vecs_3 = inverse_frequency_vectorizer.fit_transform(all_texts_tokens_3)\n",
    "\n",
    "print(inverse_frequency_vecs_1.shape)\n",
    "print(inverse_frequency_vecs_2.shape)\n",
    "print(inverse_frequency_vecs_3.shape)\n",
    "\n",
    "# Quantas noticias não possuem palavras que aparecem em pelo menos 0.1% das noticias em 'mix'\n",
    "print('Total de noticias:', inverse_frequency_vecs_3.shape[0])\n",
    "print('Noticias sem palavras que aparecem em pelo menos 0.1% das noticias:', np.sum(inverse_frequency_vecs_3.sum(axis=1) == 0))"
   ]
  }
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