Notebooks/twitter_words.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28667795-d93e-427f-9063-13ad6e133b09",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81dd7556-356c-428f-98ee-87d94c1f9a5a",
   "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>id</th>\n",
       "      <th>label</th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>@user when a father is dysfunctional and is s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>bihday your majesty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>#model   i love u take with u all the time in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>factsguide: society now    #motivation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  label                                              tweet\n",
       "0   1      0   @user when a father is dysfunctional and is s...\n",
       "1   2      0  @user @user thanks for #lyft credit i can't us...\n",
       "2   3      0                                bihday your majesty\n",
       "3   4      0  #model   i love u take with u all the time in ...\n",
       "4   5      0             factsguide: society now    #motivation"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets = pd.read_csv(\"/home/renato/Downloads/archive/train_E6oV3lV.csv\")\n",
    "tweets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27e6bdea-6fa6-4b7c-a365-219eef16a005",
   "metadata": {},
   "outputs": [],
   "source": [
    "words = tweets.tweet.sum().split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ff751d7-b97d-4141-b27a-238fb1d98a5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "freqdist = Counter(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f97bbe67-6242-4dd2-b6b9-5c95c6054248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('@user', 15856),\n",
       " ('the', 9934),\n",
       " ('to', 9758),\n",
       " ('a', 6202),\n",
       " ('i', 5240),\n",
       " ('you', 4895),\n",
       " ('and', 4821),\n",
       " ('in', 4552),\n",
       " ('for', 4427),\n",
       " ('of', 4151),\n",
       " ('is', 4088),\n",
       " ('my', 3533),\n",
       " ('on', 2542),\n",
       " ('with', 2474),\n",
       " ('this', 2377),\n",
       " ('be', 2349),\n",
       " ('it', 2080),\n",
       " ('so', 1820),\n",
       " ('all', 1802),\n",
       " ('are', 1778)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freqdist.most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d09d361b-e5d4-42c2-9614-39ee524b1eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@user',\n",
       " 'when',\n",
       " 'a',\n",
       " 'father',\n",
       " 'is',\n",
       " 'dysfunctional',\n",
       " 'and',\n",
       " 'is',\n",
       " 'so',\n",
       " 'selfish']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e8cad3c8-ce24-470e-b52e-606897cd0a31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "414005"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b9dda20-dbc4-4295-a274-04aaa47a396f",
   "metadata": {},
   "outputs": [],
   "source": [
    "setofwords = set(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7ef45e72-cac8-40bc-a66b-ce71b14680b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70523"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(setofwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ca485f2c-ba0c-42a3-bb68-4ef6e6838904",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package words to /home/renato/nltk_data...\n",
      "[nltk_data]   Package words is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('words')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0070f1bd-d290-4643-9b97-07f6cc625993",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import words as corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b23787c7-5f99-490a-9aec-2ff74d044031",
   "metadata": {},
   "outputs": [],
   "source": [
    "out_of_dictionary = [w for w in setofwords if w not in corpus.words()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "edb561ef-369c-4389-a24d-bb1d73702090",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['boxâ\\x80¦',\n",
       " 'halloween#family',\n",
       " 'when?????',\n",
       " 'wars',\n",
       " 'hea!',\n",
       " '#festivals',\n",
       " '#bahrain',\n",
       " \"'great'\",\n",
       " 'muslims\"',\n",
       " '#reboot']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_of_dictionary[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07216bde-d2ee-4027-a2c6-cb1da37cff62",
   "metadata": {},
   "outputs": [],
   "source": [
    "out_of_dictionaryex1 = \"(\\#\\w+)\"\n",
    "tweets.tweet.str.extract(regex1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1213c098-9683-4e32-ad0c-38597b176f74",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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