Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The watermark extension is already loaded. To reload it, use:\n",
      "  %reload_ext watermark\n",
      "Romell D.Z. \n",
      "last updated: 2019-08-25 \n",
      "\n",
      "tweepy 3.6.0\n",
      "scipy 1.1.0\n",
      "nltk 3.2.5\n",
      "gensim 3.4.0\n",
      "sklearn 0.20.0\n",
      "networkx 2.3\n",
      "textblob 0.15.1\n",
      "spacy 2.0.12\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a \"Romell D.Z.\" -u -d -p tweepy,scipy,nltk,gensim,sklearn,networkx,textblob,spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tweepy\n",
    "from tweepy import Stream\n",
    "\n",
    "import netrc\n",
    "\n",
    "from unidecode import unidecode\n",
    "import re\n",
    "\n",
    "from tweepy import StreamListener\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from pprint import pprint\n",
    "import pyprind\n",
    "import pandas as pd\n",
    "\n",
    "import random\n",
    "from scipy.stats import beta as beta_distribution\n",
    "import numpy as np\n",
    "\n",
    "from nltk.corpus import stopwords \n",
    "from nltk import word_tokenize\n",
    "from nltk.data import load\n",
    "from nltk.stem import SnowballStemmer\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "import string\n",
    "\n",
    "import gensim\n",
    "from gensim import corpora\n",
    "\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim  # don't skip this\n",
    "\n",
    "from gensim.models.ldamodel import LdaModel\n",
    "from string import punctuation\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from textblob import Word\n",
    "from textblob import TextBlob\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import advertools as adv\n",
    "\n",
    "import networkx as nx\n",
    "\n",
    "import spacy\n",
    "from spacy import displacy\n",
    "nlp = spacy.load('es_core_news_sm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "auth = netrc.netrc()\n",
    "ckey,_,csecret=auth.authenticators('tweet_api')\n",
    "atoken,_,asecret=auth.authenticators('tweet_secret')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [BoundingBox](https://boundingbox.klokantech.com/) CSV RAW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LIMA_GEO_LOCATION_BOUNDING_BOX = [-77.1785277831,-12.1531578397,-76.8967618806,-11.9288928156]\n",
    "LIMA_GEO_LOCATION_BOUNDING_BOX = [ -81.802362,-17.525482, -69.774343,-3.226278]\n",
    "auth = tweepy.OAuthHandler(ckey, csecret)\n",
    "auth.set_access_token(atoken, asecret)\n",
    "api = tweepy.API(auth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUMBER_OF_TWEETS = 1000\n",
    "pbar = pyprind.ProgBar(NUMBER_OF_TWEETS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FiniteStreamListener(StreamListener):\n",
    "    \n",
    "    def __init__(self, number_of_tweets):\n",
    "        self.number_of_tweets = number_of_tweets\n",
    "        self.tweets = []\n",
    "        self.tweets_dict = []\n",
    "        super(FiniteStreamListener,self).__init__()\n",
    "        \n",
    "    def on_status(self, status):\n",
    "        if len(self.tweets) < self.number_of_tweets:\n",
    "            self.tweets_dict.append(status._json)\n",
    "            place = status._json['place']['name'] if(status._json['place']) else \"\"\n",
    "            self.tweets.append({'date':status.created_at,\n",
    "                                'text':status.text,\n",
    "                                'location':place,\n",
    "                                'followers':status._json['user']['followers_count']})\n",
    "            pbar.update()\n",
    "        else:\n",
    "            return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "finite_stream_listener = FiniteStreamListener(number_of_tweets=NUMBER_OF_TWEETS)\n",
    "streaming_api = Stream(auth=auth, listener=finite_stream_listener,timeout=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The streaming API doesn't allow to filter by location AND keyword simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "EMOTICONS = \">:] :-) :) :o) :] :3 :c) :> =] 8) =) :} :^) \"\n",
    "EMOTICONS = EMOTICONS.strip().split(' ')\n",
    "# streaming_api.filter(track=EMOTICONS,async=True)\n",
    "streaming_api.filter(locations=LIMA_GEO_LOCATION_BOUNDING_BOX,async=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0% [##############################] 100% | ETA: 00:00:00\n",
      "Total time elapsed: 01:05:42\n"
     ]
    }
   ],
   "source": [
    "print(len(finite_stream_listener.tweets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('tweets_dict',finite_stream_listener.tweets_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_lowercase(tweet):\n",
    "    return tweet.lower()\n",
    "\n",
    "def remove_diacritics(tweet):\n",
    "    return unidecode(tweet)\n",
    "\n",
    "def remove_non_alpha_characters(tweet):\n",
    "    return ''.join(character for character in tweet if character.isalpha() or character == ' ')\n",
    "\n",
    "def remove_web_site(tweet):\n",
    "    return re.sub(r'http\\w+', '', tweet)#, flags=re.MULTILINE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_df = pd.DataFrame.from_dict(finite_stream_listener.tweets)\n",
    "tweets_df.rename(columns={'text':'Tweets'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_df['word_count'] = tweets_df['Tweets'].apply(lambda x: len(str(x).split(\" \")))\n",
    "tweets_df['char_count'] = tweets_df['Tweets'].str.len()\n",
    "\n",
    "def avg_word(sentence):\n",
    "    words = sentence.split()\n",
    "    return (sum(len(word) for word in words)/len(words))\n",
    "\n",
    "tweets_df['avg_word'] = tweets_df['Tweets'].apply(lambda x: avg_word(x))\n",
    "tweets_df['hastags'] = tweets_df['Tweets'].apply(lambda x: len([x for x in x.split() if x.startswith('#')]))\n",
    "tweets_df['numerics'] = tweets_df['Tweets'].apply(lambda x: len([x for x in x.split() if x.isdigit()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_tweets = list(tweets_df['Tweets'])\n",
    "for cleaning_function in \\\n",
    "    [make_lowercase, \n",
    "#      remove_diacritics,\n",
    "     remove_non_alpha_characters,\n",
    "     remove_web_site]:\n",
    "    cleaned_tweets = [cleaning_function(tweet) for tweet in cleaned_tweets]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ultima noche de semanademayordomia en la iasdesperanza aprendiendo sobre la importancia de ser fiel a dios con l ',\n",
       " 'não existe boy legal em salvador asklgbt',\n",
       " 'movistarvoleype vamos perú no podemos flaquear más adelante los equipos son más fuertes sabemos de su espíritu de lucha arriba perú',\n",
       " 'noticiamerica tanto lío por ese pequeño lapsus',\n",
       " 'pobre chica la cuerda siempre se rompe por el lado mas débil']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.sample(cleaned_tweets,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nuestra palabra clave aparece: 41 veces\n",
      "Nuestra palabra clave apareció en: 4.1% de los tweets\n"
     ]
    }
   ],
   "source": [
    "KEYWORD='lima'\n",
    "number_of_occurences = sum(KEYWORD in tweet for tweet in cleaned_tweets)\n",
    "print('Nuestra palabra clave aparece: {} veces'.format(number_of_occurences))\n",
    "print('Nuestra palabra clave apareció en: {}% de los tweets'.format(100 * number_of_occurences/NUMBER_OF_TWEETS))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cooking with fire in peru today cookingclass seerundo lima peru '\n",
      " 'southamerica  lima peru ',\n",
      " 'shopping for our cooking class today mercado seerundo lima peru '\n",
      " 'southamerica  mercado n de surquillo ',\n",
      " 'im at the beer place in lima  ',\n",
      " 'im at centro cultural ricardo palma  munimiraflores in miraflores lima ',\n",
      " 'estoy listo y esperándote barbon amado  una nueva jornada juntos hsmn '\n",
      " 'octubremesmorado en sheraton lima hot ']\n"
     ]
    }
   ],
   "source": [
    "pprint([tweet for tweet in cleaned_tweets if KEYWORD in tweet][:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index de los 10 tweets:[6, 23, 103, 107, 116, 144, 149, 173, 211, 219, 237, 252, 254, 260, 277, 330, 373, 389, 392, 411, 421, 512, 522, 550, 653, 667, 684, 726, 727, 736, 743, 749, 821, 867, 877, 879, 884, 907, 912, 927, 969]\n"
     ]
    }
   ],
   "source": [
    "indices_of_tweets_containing_keyword=[index for index, tweet in enumerate(cleaned_tweets) if KEYWORD in tweet]\n",
    "print('index de los 10 tweets:%s'%(indices_of_tweets_containing_keyword))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "distances_between_indices_of_tweets_containing_keyword = [\n",
    "    second_index - first_index for first_index, second_index in \\\n",
    "        zip(indices_of_tweets_containing_keyword[:-1], indices_of_tweets_containing_keyword[1:])\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(distances_between_indices_of_tweets_containing_keyword).hist()\n",
    "plt.savefig('snapshot/lima_tweets_hist.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 1 + number_of_occurences\n",
    "beta = 1 + (NUMBER_OF_TWEETS - number_of_occurences)\n",
    "\n",
    "x_values = np.linspace(0, 1, 1002)[1:-1]\n",
    "pdf_y_values = beta_distribution(alpha, beta).pdf(x_values)\n",
    "cdf_y_values = np.cumsum(pdf_y_values) / np.sum(pdf_y_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 6))\n",
    "plt.subplot(121)\n",
    "plt.plot(x_values, pdf_y_values, label=(r'$\\alpha=%.1f,\\ \\beta=%.1f$' % (alpha, beta)))\n",
    "\n",
    "plt.xlim(0, 1)\n",
    "plt.xlabel('Probability of tweet containing keyword')\n",
    "plt.ylabel('Probability density')\n",
    "plt.title('Beta Distribution PDF')\n",
    "plt.legend(loc=1)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(x_values, cdf_y_values)\n",
    "plt.xlim(0, 1)\n",
    "plt.ylim(0, 1.005)\n",
    "plt.yticks(np.linspace(0, 1, 21))\n",
    "plt.xlabel('Probability of tweet containing keyword')\n",
    "plt.ylabel('Cumulative probability')\n",
    "plt.title('Beta Distribution CDF')\n",
    "plt.savefig('snapshot/Beta Distribution CDF.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ix = [n for n,b in enumerate((cdf_y_values>.5)&(cdf_y_values<.95)) if b]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "range_ =cdf_y_values[ix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.c_[x_values[ix],cdf_y_values[ix]]\n",
    "max_ix,_ = np.unravel_index(a.argmax(), a.shape)\n",
    "min_ix,_ = np.unravel_index(a.argmin(), a.shape)\n",
    "fifth_percentile, ninety_fifth_percentile = x_values[max_ix],x_values[min_ix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Con 90% de certeza digamos que la verdadera probabilidad se encuentra entre: 0.010989011 y 0.000999001\n"
     ]
    }
   ],
   "source": [
    "print('Con 90% de certeza digamos que la verdadera probabilidad se encuentra entre: {} y {}'.format(\n",
    "    round(fifth_percentile, 10), round(ninety_fifth_percentile, 10)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La probabilidad que la verdadera probabilidad es > .1 es: 1.9441115384211116e-12\n"
     ]
    }
   ],
   "source": [
    "def compute_total_probability_that_probability_less_than_p(p):\n",
    "    return max(cumulative_prob for cumulative_prob, x_value in zip(cdf_y_values, x_values) if x_value < p)\n",
    "\n",
    "print('La probabilidad que la verdadera probabilidad es > .1 es: {}'.format(\n",
    "    1 - compute_total_probability_that_probability_less_than_p(.1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tweets_df.groupby('location').agg({'location':len}).sort_values('location').iloc[-15:].plot(kind='barh')\n",
    "plt.title('Most Frequent locations')\n",
    "plt.xlabel('Cantidad')\n",
    "plt.tight_layout()\n",
    "plt.savefig('snapshot/most Frequent locations.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_df.to_csv('lima_tweets.csv',index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_df = pd.read_csv('lima_tweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop =stopwords.words('spanish') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_complete = tweets_df.Tweets.values\n",
    "exclude = set(string.punctuation) \n",
    "lemma = WordNetLemmatizer()\n",
    "def clean(doc):\n",
    "    stop_free = \" \".join([i for i in doc.lower().split() if i not in stop])\n",
    "    punc_free = ''.join(ch for ch in stop_free if ch not in exclude)\n",
    "    normalized = \" \".join(lemma.lemmatize(word) for word in punc_free.split())\n",
    "    return normalized\n",
    "\n",
    "doc_clean = [clean(doc).split() for doc in doc_complete]   \n",
    "tweets_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_df['Tweets_clean'] = pd.Series(doc_clean).apply(lambda x:' '.join(x))\n",
    "tweets_df['word_count'] = tweets_df['Tweets_clean'].apply(lambda x: len(str(x).split(\" \")))\n",
    "tweets_df['char_count'] = tweets_df['Tweets_clean'].str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>date</th>\n",
       "      <th>followers</th>\n",
       "      <th>location</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>word_count</th>\n",
       "      <th>char_count</th>\n",
       "      <th>avg_word</th>\n",
       "      <th>hastags</th>\n",
       "      <th>numerics</th>\n",
       "      <th>Tweets_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-20 01:57:03</td>\n",
       "      <td>92</td>\n",
       "      <td>Lima</td>\n",
       "      <td>@elpanfletope @AlanGarciaPeru chancho no come ...</td>\n",
       "      <td>5</td>\n",
       "      <td>48</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>elpanfletope alangarciaperu chancho come chancho</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-10-20 01:57:10</td>\n",
       "      <td>32</td>\n",
       "      <td>Lince</td>\n",
       "      <td>@exitosape @JulianaOxenford A DONDE DICE ...</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>9.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>exitosape julianaoxenford dice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10-20 01:57:12</td>\n",
       "      <td>195</td>\n",
       "      <td>Brasil</td>\n",
       "      <td>@anajuliachs kkkk 1 mês</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>anajuliachs kkkk 1 mês</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-10-20 01:57:17</td>\n",
       "      <td>228</td>\n",
       "      <td>Chimbote</td>\n",
       "      <td>Ultima noche de #SemanaDeMayordomia en la @IAS...</td>\n",
       "      <td>11</td>\n",
       "      <td>105</td>\n",
       "      <td>8.636364</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>ultima noche semanademayordomia iasdesperanza ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-10-20 01:57:18</td>\n",
       "      <td>123</td>\n",
       "      <td>San Miguel</td>\n",
       "      <td>@fernando_roman1 Jajaja</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fernandoroman1 jajaja</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date  followers    location  \\\n",
       "0  2018-10-20 01:57:03         92        Lima   \n",
       "1  2018-10-20 01:57:10         32       Lince   \n",
       "2  2018-10-20 01:57:12        195      Brasil   \n",
       "3  2018-10-20 01:57:17        228    Chimbote   \n",
       "4  2018-10-20 01:57:18        123  San Miguel   \n",
       "\n",
       "                                              Tweets  word_count  char_count  \\\n",
       "0  @elpanfletope @AlanGarciaPeru chancho no come ...           5          48   \n",
       "1       @exitosape @JulianaOxenford A DONDE DICE ...           3          30   \n",
       "2                            @anajuliachs kkkk 1 mês           4          22   \n",
       "3  Ultima noche de #SemanaDeMayordomia en la @IAS...          11         105   \n",
       "4                            @fernando_roman1 Jajaja           2          21   \n",
       "\n",
       "    avg_word  hastags  numerics  \\\n",
       "0   8.800000        0         0   \n",
       "1   9.333333        0         0   \n",
       "2   4.750000        0         1   \n",
       "3   8.636364        1         0   \n",
       "4  10.000000        0         0   \n",
       "\n",
       "                                        Tweets_clean  \n",
       "0   elpanfletope alangarciaperu chancho come chancho  \n",
       "1                     exitosape julianaoxenford dice  \n",
       "2                             anajuliachs kkkk 1 mês  \n",
       "3  ultima noche semanademayordomia iasdesperanza ...  \n",
       "4                              fernandoroman1 jajaja  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def avg_word(sentence):\n",
    "    words = sentence.split()\n",
    "    if len(words)==0:\n",
    "        return 0\n",
    "    return (sum(len(word) for word in words)/len(words))\n",
    "\n",
    "tweets_df['avg_word'] = tweets_df['Tweets_clean'].apply(lambda x: avg_word(x))\n",
    "tweets_df['hastags'] = tweets_df['Tweets'].apply(lambda x: len([x for x in x.split() if x.startswith('#')]))\n",
    "tweets_df['numerics'] = tweets_df['Tweets'].apply(lambda x: len([x for x in x.split() if x.isdigit()]))\n",
    "tweets_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq = pd.Series(' '.join(tweets_df['Tweets_clean']).split()).value_counts()[:10]\n",
    "freq.plot(kind='barh')\n",
    "plt.title('Most Frequent words')\n",
    "plt.xlabel('Count')\n",
    "plt.tight_layout()\n",
    "plt.savefig('snapshot/most Frequent words.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    elpanfletope alangarciaperu chancho come chancho\n",
      "1                      exitosape julianaoxenford dice\n",
      "Name: Tweets_clean, dtype: object\n"
     ]
    }
   ],
   "source": [
    "pprint(tweets_df['Tweets_clean'][:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary = corpora.Dictionary(tweets_df['Tweets_clean'].apply(lambda x:x.split()))\n",
    "\n",
    "# Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above.\n",
    "doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[(0, 1), (1, 2), (2, 1), (3, 1)], [(4, 1), (5, 1), (6, 1)]]\n"
     ]
    }
   ],
   "source": [
    "pprint(doc_term_matrix[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lda = gensim.models.ldamodel.LdaModel\n",
    "ldamodel = Lda(doc_term_matrix, num_topics=3, id2word = dictionary, passes=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, '0.006*\"peru\" + 0.006*\"lima\" + 0.005*\"–\"'),\n",
      " (1, '0.004*\"lima\" + 0.004*\"in\" + 0.003*\"fiscal\"'),\n",
      " (2, '0.005*\"si\" + 0.003*\"canaln\" + 0.002*\"amor\"')]\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "pprint(ldamodel.print_topics(num_topics=3, num_words=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from gensim.test.utils import datapath\n",
    "# fname = datapath(\"lda_lima_tweet_model\")\n",
    "ldamodel.save(\"lda_lima_tweet_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models.ldamodel import LdaModel\n",
    "ldamodel = LdaModel.load(\"lda_lima_tweet_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_lda = ldamodel[doc_term_matrix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perplexity:  -8.825555626039103\n"
     ]
    }
   ],
   "source": [
    "print('Perplexity: ', ldamodel.log_perplexity(doc_term_matrix))  # a measure of how good the model is. lower the better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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0.8065466284751892, 0.24196398258209229, 0.9936856627464294, 0.8144999742507935, 0.9773145914077759, 0.8038736581802368, 0.9852656126022339], \"Term\": [\"22d\\u00edasparaveracach\\u00e9\", \"22d\\u00edasparaveracach\\u00e9\", \"abacojuridico\", \"acordo\", \"acordo\", \"ahora\", \"ahora\", \"ahora\", \"alguien\", \"alguien\", \"almuerzo\", \"alvarezrodrich\", \"alvarosarco\", \"amigo\", \"amigo\", \"amor\", \"amor\", \"anapaularenault\", \"andrizek\", \"anticuchos\", \"apristas\", \"aqu\\u00ed\", \"aqu\\u00ed\", \"aqu\\u00ed\", \"arimborgotammy\", \"as\\u00ed\", \"at\", \"beb\\u00e9s\", \"bien\", \"bien\", \"bien\", \"borracho\", \"bueno\", \"buggy\", \"calleypocheoffi\", \"canaln\", \"canaln\", \"canaln\", \"casa\", \"casa\", \"casa\", \"caso\", \"caso\", \"cesarnakazaki\", \"cnnee\", \"cola\", \"c\\u00e1ncer\", \"da\", \"da\", \"da\", \"despu\\u00e9s\", \"dia\", \"do\", \"do\", \"do\", \"domingo\", \"domingo\", \"em\", \"em\", \"em\", \"en\\u2026\", \"esperamos\", \"eu\", \"eu\", \"eu\", \"evaluar\", \"experienciasunicas\", \"favor\", \"favor\", \"favor\", \"final\", \"fiscal\", \"fiscal\", \"frankazeredo\", \"franpetrozzi\", \"franpetrozzi\", \"fujimoristas\", \"furg\\u00f3n\", \"gabrielitaaa10\", \"gabrielitaaa10\", \"gracias\", \"gracias\", \"gracias\", \"gt\", \"gusta\", \"haces\", \"haces\", \"hoy\", \"hoy\", \"hoy\", \"httpstcogcgczwtd4z\", \"httpstcogcgczwtd4z\", \"i\", \"i\", \"i\", \"idlr\", \"idlr\", \"idlr\", \"im\", \"im\", \"in\", \"in\", \"iogrr\", \"irvingtabares\", \"jaja\", \"jajaja\", \"jefersonine9\", \"jhersin091\", \"jos\\u00e9\", \"keikofujimori\", \"keikofujimori\", \"lima\", \"lima\", \"lucha\", \"lucha\", \"ma\", \"ma\", \"ma\", \"mama\", \"marcelonevesmor\", \"mart\\u00edn\", \"mart\\u00edn\", \"mart\\u00edn\", \"mauro\", \"mauro\", \"medina\", \"mesmorado\", \"milibrujita\", \"mininterperu\", \"mininterperu\", \"mira\", \"movistarvoleype\", \"msalazarcongres\", \"msalazarcongres\", \"mulderrctm\", \"nettoaqa\", \"nombre\", \"of\", \"of\", \"paopps\", \"patas\", \"pbodigital\\u2026\", \"perfeito\", \"perfeito\", \"peru\", \"peru\", \"per\\u00fa\", \"per\\u00fa\", \"per\\u00fa\", \"pocos\", \"poder\", \"poder\", \"prisi\\u00f3n\", \"procesion\", \"puede\", \"puede\", \"pueden\", \"p\\u00e9rez\", \"q\", \"q\", \"q\", \"qua\", \"quando\", \"quiero\", \"rafaelsatim\", \"record\", \"retuit\", \"rio\", \"ron\", \"rosamariabartra\", \"rosamariabartra\", \"rosamariabartra\", \"rppnoticias\", \"rppnoticias\", \"sabe\", \"san\", \"san\", \"seguridad\", \"ser\", \"ser\", \"ser\", \"se\\u00f1ordelosmilagros\", \"si\", \"si\", \"si\", \"siento\", \"sigam\", \"solo\", \"solo\", \"solo\", \"sunset\\ud83c\\udf05\", \"s\\u00f3\", \"tae\", \"tal\", \"tan\", \"tan\", \"turrondedo\\u00f1apepa\", \"t\\u00e1\", \"va\", \"va\", \"va\", \"vamos\", \"ver\", \"ver\", \"ver\", \"veromendozaf\", \"vez\", \"vez\", \"video\", \"video\", \"viendo\", \"viendo\", \"viernes\", \"vizcarra\", \"voc\\u00ea\", \"yuridivinicius\", \"zacapa\", \"\\u00b7\", \"\\u00e9\", \"\\u00e9\", \"\\u2013\", \"\\u2764\\ufe0f\", \"\\ud83d\\udc95\\u2728\", \"\\ud83d\\udc9c\", \"\\ud83d\\ude44\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 2, 3]};\n",
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      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "0     -0.102617  0.026479       1        1  34.787315\n",
       "1      0.027390 -0.098442       2        1  32.855946\n",
       "2      0.075227  0.071963       3        1  32.356747, topic_info=     Category       Freq                Term      Total  loglift  logprob\n",
       "term                                                                     \n",
       "34    Default  18.000000                peru  18.000000  30.0000  30.0000\n",
       "2257  Default  13.000000                   –  13.000000  29.0000  29.0000\n",
       "33    Default  27.000000                lima  27.000000  28.0000  28.0000\n",
       "32    Default  12.000000                  in  12.000000  27.0000  27.0000\n",
       "57    Default  10.000000              fiscal  10.000000  26.0000  26.0000\n",
       "644   Default  12.000000                   é  12.000000  25.0000  25.0000\n",
       "530   Default   6.000000                sabe   6.000000  24.0000  24.0000\n",
       "570   Default   6.000000                josé   6.000000  23.0000  23.0000\n",
       "455   Default   6.000000              quiero   6.000000  22.0000  22.0000\n",
       "162   Default   7.000000                 san   7.000000  21.0000  21.0000\n",
       "1727  Default   6.000000                 así   6.000000  20.0000  20.0000\n",
       "779   Default   5.000000                  at   5.000000  19.0000  19.0000\n",
       "887   Default   5.000000          mulderrctm   5.000000  18.0000  18.0000\n",
       "658   Default   8.000000                amor   8.000000  17.0000  17.0000\n",
       "1020  Default   4.000000                 dia   4.000000  16.0000  16.0000\n",
       "23    Default   4.000000              jajaja   4.000000  15.0000  15.0000\n",
       "2099  Default   4.000000               pérez   4.000000  14.0000  14.0000\n",
       "2784  Default   4.000000       irvingtabares   4.000000  13.0000  13.0000\n",
       "882   Default   4.000000               vamos   4.000000  12.0000  12.0000\n",
       "2378  Default   4.000000            vizcarra   4.000000  11.0000  11.0000\n",
       "537   Default   9.000000             domingo   9.000000  10.0000  10.0000\n",
       "1767  Default   4.000000                 tal   4.000000   9.0000   9.0000\n",
       "2081  Default   4.000000             viernes   4.000000   8.0000   8.0000\n",
       "60    Default   4.000000           seguridad   4.000000   7.0000   7.0000\n",
       "2376  Default   4.000000               patas   4.000000   6.0000   6.0000\n",
       "2379  Default   4.000000              zacapa   4.000000   5.0000   5.0000\n",
       "2370  Default   4.000000            almuerzo   4.000000   4.0000   4.0000\n",
       "2377  Default   4.000000                 ron   4.000000   3.0000   3.0000\n",
       "2371  Default   4.000000            borracho   4.000000   2.0000   2.0000\n",
       "1327  Default   4.000000       abacojuridico   4.000000   1.0000   1.0000\n",
       "...       ...        ...                 ...        ...      ...      ...\n",
       "1291   Topic3   2.024631     movistarvoleype   2.437233   0.9429  -7.1358\n",
       "1579   Topic3   2.024623              nombre   2.437234   0.9429  -7.1358\n",
       "2753   Topic3   2.024604               bebés   2.437234   0.9429  -7.1358\n",
       "2431   Topic3   2.024585                jaja   2.437234   0.9428  -7.1358\n",
       "81     Topic3   2.024582        veromendozaf   2.437234   0.9428  -7.1358\n",
       "425    Topic3   2.024570               pocos   2.437235   0.9428  -7.1358\n",
       "540    Topic3   2.024568             prisión   2.437235   0.9428  -7.1358\n",
       "82     Topic3   3.857558                caso   4.873332   0.8946  -6.4912\n",
       "479    Topic3   3.860428                 vez   4.882683   0.8934  -6.4904\n",
       "658    Topic3   6.305642                amor   8.558960   0.8228  -5.9997\n",
       "1473   Topic3   3.247496             alguien   4.265806   0.8556  -6.6633\n",
       "469    Topic3   3.896584               favor   5.495749   0.7845  -6.4811\n",
       "416    Topic3  13.073942                  si  25.710005   0.4521  -5.2706\n",
       "1556   Topic3   3.835145       keikofujimori   5.487342   0.7701  -6.4970\n",
       "405    Topic3   4.463663               amigo   6.707940   0.7210  -6.3452\n",
       "272    Topic3   5.111762                  em   8.561710   0.6126  -6.2096\n",
       "80     Topic3   5.846062                 ver  10.382248   0.5540  -6.0754\n",
       "626    Topic3   4.492486                perú   7.340539   0.6373  -6.3388\n",
       "498    Topic3   3.892911                bien   6.119231   0.6761  -6.4820\n",
       "99     Topic3   5.736736                  da  12.296143   0.3660  -6.0943\n",
       "205    Topic3   6.881176              canaln  19.667906   0.0781  -5.9124\n",
       "3933   Topic3   3.278217  httpstcogcgczwtd4z   4.879065   0.7307  -6.6539\n",
       "3935   Topic3   3.278217            perfeito   4.879065   0.7307  -6.6539\n",
       "3930   Topic3   3.278217              acordo   4.879065   0.7307  -6.6539\n",
       "569    Topic3   4.493968                  eu  11.041849   0.2294  -6.3385\n",
       "568    Topic3   5.034446                  do  16.558788  -0.0623  -6.2249\n",
       "212    Topic3   3.859593                solo  10.473749   0.1300  -6.4906\n",
       "515    Topic3   3.840937                  va  13.592248  -0.1354  -6.4955\n",
       "77     Topic3   3.824279                   q  19.718220  -0.5118  -6.4998\n",
       "480    Topic3   3.481355               ahora  13.538285  -0.2298  -6.5938\n",
       "\n",
       "[192 rows x 6 columns], token_table=      Topic      Freq                 Term\n",
       "term                                      \n",
       "450       2  0.699132  22díasparaveracaché\n",
       "450       3  0.233044  22díasparaveracaché\n",
       "1327      1  0.918411        abacojuridico\n",
       "3930      2  0.204957               acordo\n",
       "3930      3  0.614872               acordo\n",
       "480       1  0.369323                ahora\n",
       "480       2  0.369323                ahora\n",
       "480       3  0.221594                ahora\n",
       "1473      2  0.234422              alguien\n",
       "1473      3  0.703267              alguien\n",
       "2370      1  0.918410             almuerzo\n",
       "2493      3  0.821428       alvarezrodrich\n",
       "2185      2  0.814533          alvarosarco\n",
       "405       2  0.298154                amigo\n",
       "405       3  0.596308                amigo\n",
       "658       1  0.233673                 amor\n",
       "658       3  0.701020                 amor\n",
       "180       1  0.918436      anapaularenault\n",
       "1328      1  0.918411             andrizek\n",
       "4433      1  0.803874           anticuchos\n",
       "2473      3  0.821432             apristas\n",
       "880       1  0.148094                 aquí\n",
       "880       2  0.444282                 aquí\n",
       "880       3  0.296188                 aquí\n",
       "2186      2  0.977376       arimborgotammy\n",
       "1727      1  0.963786                  así\n",
       "779       2  0.905134                   at\n",
       "2753      3  0.820602                bebés\n",
       "498       1  0.163419                 bien\n",
       "498       2  0.163419                 bien\n",
       "...     ...       ...                  ...\n",
       "322       2  0.721609                  tan\n",
       "4437      1  0.803874     turrondedoñapepa\n",
       "839       1  0.918474                   tá\n",
       "515       1  0.588571                   va\n",
       "515       2  0.073571                   va\n",
       "515       3  0.294285                   va\n",
       "882       3  0.821779                vamos\n",
       "80        1  0.096318                  ver\n",
       "80        2  0.385273                  ver\n",
       "80        3  0.577910                  ver\n",
       "81        3  0.820602         veromendozaf\n",
       "479       1  0.204805                  vez\n",
       "479       3  0.819222                  vez\n",
       "146       1  0.804958                video\n",
       "146       2  0.201239                video\n",
       "1248      1  0.718020               viendo\n",
       "1248      3  0.179505               viendo\n",
       "2081      2  0.930939              viernes\n",
       "2378      1  1.004264             vizcarra\n",
       "1130      1  0.803936                 você\n",
       "1336      1  0.918411       yuridivinicius\n",
       "2379      1  0.918410               zacapa\n",
       "1410      2  0.977376                    ·\n",
       "644       1  0.806547                    é\n",
       "644       3  0.241964                    é\n",
       "2257      1  0.993686                    –\n",
       "1504      2  0.814500                   ❤️\n",
       "1664      2  0.977315                   💕✨\n",
       "4438      1  0.803874                    💜\n",
       "983       3  0.985266                    🙄\n",
       "\n",
       "[225 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[1, 2, 3])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.enable_notebook()\n",
    "vis = pyLDAvis.gensim.prepare(ldamodel, doc_term_matrix, dictionary)\n",
    "vis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[], ['dice'], [], ['aprendiendo'], []]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
    "    texts_out = []\n",
    "    for sent in texts:\n",
    "        doc_ = nlp(sent)\n",
    "        texts_out.append(list(set([str(c.head) for c in doc_ if c.head.tag_.startswith(tuple(allowed_postags))])))\n",
    "    return texts_out\n",
    "\n",
    "lemmatization(tweets_df['Tweets_clean'][:5],['VERB'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def join_comma(row_list):\n",
    "    if row_list == []:\n",
    "        return np.NaN\n",
    "    else:\n",
    "        return ', '.join(row_list)\n",
    "\n",
    "tweets_df['ACTIONS']=pd.Series(lemmatization(tweets_df['Tweets_clean'],['VERB'])).apply(join_comma)\n",
    "tweets_df['NOUNS']=pd.Series(lemmatization(tweets_df['Tweets_clean'],['NOUN'])).apply(join_comma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweets_clean</th>\n",
       "      <th>NOUNS</th>\n",
       "      <th>ACTIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elpanfletope alangarciaperu chancho come chancho</td>\n",
       "      <td>elpanfletope</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>exitosape julianaoxenford dice</td>\n",
       "      <td>exitosape</td>\n",
       "      <td>dice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anajuliachs kkkk 1 mês</td>\n",
       "      <td>mês</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ultima noche semanademayordomia iasdesperanza ...</td>\n",
       "      <td>noche</td>\n",
       "      <td>aprendiendo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fernandoroman1 jajaja</td>\n",
       "      <td>jajaja</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Tweets_clean         NOUNS  \\\n",
       "0   elpanfletope alangarciaperu chancho come chancho  elpanfletope   \n",
       "1                     exitosape julianaoxenford dice     exitosape   \n",
       "2                             anajuliachs kkkk 1 mês           mês   \n",
       "3  ultima noche semanademayordomia iasdesperanza ...         noche   \n",
       "4                              fernandoroman1 jajaja        jajaja   \n",
       "\n",
       "       ACTIONS  \n",
       "0          NaN  \n",
       "1         dice  \n",
       "2          NaN  \n",
       "3  aprendiendo  \n",
       "4          NaN  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_df[['Tweets_clean','NOUNS','ACTIONS']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>date</th>\n",
       "      <th>followers</th>\n",
       "      <th>location</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>word_count</th>\n",
       "      <th>char_count</th>\n",
       "      <th>avg_word</th>\n",
       "      <th>hastags</th>\n",
       "      <th>numerics</th>\n",
       "      <th>Tweets_clean</th>\n",
       "      <th>ACTIONS</th>\n",
       "      <th>NOUNS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-20 01:57:03</td>\n",
       "      <td>92</td>\n",
       "      <td>Lima</td>\n",
       "      <td>@elpanfletope @AlanGarciaPeru chancho no come ...</td>\n",
       "      <td>5</td>\n",
       "      <td>48</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>elpanfletope alangarciaperu chancho come chancho</td>\n",
       "      <td>NaN</td>\n",
       "      <td>elpanfletope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-10-20 01:57:10</td>\n",
       "      <td>32</td>\n",
       "      <td>Lince</td>\n",
       "      <td>@exitosape @JulianaOxenford A DONDE DICE ...</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>9.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>exitosape julianaoxenford dice</td>\n",
       "      <td>dice</td>\n",
       "      <td>exitosape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10-20 01:57:12</td>\n",
       "      <td>195</td>\n",
       "      <td>Brasil</td>\n",
       "      <td>@anajuliachs kkkk 1 mês</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>anajuliachs kkkk 1 mês</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mês</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-10-20 01:57:17</td>\n",
       "      <td>228</td>\n",
       "      <td>Chimbote</td>\n",
       "      <td>Ultima noche de #SemanaDeMayordomia en la @IAS...</td>\n",
       "      <td>11</td>\n",
       "      <td>105</td>\n",
       "      <td>8.636364</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>ultima noche semanademayordomia iasdesperanza ...</td>\n",
       "      <td>aprendiendo</td>\n",
       "      <td>noche</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-10-20 01:57:18</td>\n",
       "      <td>123</td>\n",
       "      <td>San Miguel</td>\n",
       "      <td>@fernando_roman1 Jajaja</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fernandoroman1 jajaja</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jajaja</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date  followers    location  \\\n",
       "0  2018-10-20 01:57:03         92        Lima   \n",
       "1  2018-10-20 01:57:10         32       Lince   \n",
       "2  2018-10-20 01:57:12        195      Brasil   \n",
       "3  2018-10-20 01:57:17        228    Chimbote   \n",
       "4  2018-10-20 01:57:18        123  San Miguel   \n",
       "\n",
       "                                              Tweets  word_count  char_count  \\\n",
       "0  @elpanfletope @AlanGarciaPeru chancho no come ...           5          48   \n",
       "1       @exitosape @JulianaOxenford A DONDE DICE ...           3          30   \n",
       "2                            @anajuliachs kkkk 1 mês           4          22   \n",
       "3  Ultima noche de #SemanaDeMayordomia en la @IAS...          11         105   \n",
       "4                            @fernando_roman1 Jajaja           2          21   \n",
       "\n",
       "    avg_word  hastags  numerics  \\\n",
       "0   8.800000        0         0   \n",
       "1   9.333333        0         0   \n",
       "2   4.750000        0         1   \n",
       "3   8.636364        1         0   \n",
       "4  10.000000        0         0   \n",
       "\n",
       "                                        Tweets_clean      ACTIONS  \\\n",
       "0   elpanfletope alangarciaperu chancho come chancho          NaN   \n",
       "1                     exitosape julianaoxenford dice         dice   \n",
       "2                             anajuliachs kkkk 1 mês          NaN   \n",
       "3  ultima noche semanademayordomia iasdesperanza ...  aprendiendo   \n",
       "4                              fernandoroman1 jajaja          NaN   \n",
       "\n",
       "          NOUNS  \n",
       "0  elpanfletope  \n",
       "1     exitosape  \n",
       "2           mês  \n",
       "3         noche  \n",
       "4        jajaja  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_df.to_csv('tweets_solutions.csv',index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets_df = pd.read_csv('tweets_solutions.csv')\n",
    "tweets_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1                         exitosape julianaoxenford dice\n",
       "3      ultima noche semanademayordomia iasdesperanza ...\n",
       "6      cooking with fire in peru today cookingclass s...\n",
       "7      show música vivo tecnópolis cierra temporada 2...\n",
       "8                      maldita perra perdón alteré 💁🏻‍♀️\n",
       "9      mininterperu pcmperu vizcarrhagan algo denle s...\n",
       "10     🇪🇨🇪🇨quito ecuador 🇪🇨🇪🇨 vemos mañana eslae http...\n",
       "11     sasha71396634 ernestojx hninurta gabospeed94 k...\n",
       "12     ¡conoce proceso exhumación caso mascarilla com...\n",
       "15     ronaldomendes triste fim da baleia bora fazer ...\n",
       "17     mt colrichardkemp pt quinze mulheres disseram ...\n",
       "19     kerch mourns victim of college massacre a surv...\n",
       "21     pensar convento san francisco reposan restos f...\n",
       "22     reaccionando ❌te puse perder ❌ javierramireze ...\n",
       "23     shopping for our cooking class today mercado s...\n",
       "26     acompañamos autores alvarobisama marcelo mella...\n",
       "28     sturt0208 canaln martinvizcarrac arrugador fuj...\n",
       "31     🎗️la prevención mejor tratamiento🎗️🙆‍♀️ cáncer...\n",
       "32     tatahcomenta onde tendo live sigam anapaularen...\n",
       "34     richardacunan tarde reacción sensatez tiempo h...\n",
       "35     tô levando tempo necessário p entender preciso...\n",
       "36     sí devolvió libropero parte cumpliócon debíaah...\n",
       "39     cara palo jc rodríguez preguntando álvaro sala...\n",
       "40     capital967 mauriciomulder vergüenza dan person...\n",
       "41     story time i almost made my mom amp i late for...\n",
       "43     malibulox pra mim não aparece sigam anapaulare...\n",
       "44     210002ws2080 wind225°at00kmh gust00kmh t176°cd...\n",
       "45     sentimientos encontrados respecto quedo 3 cosa...\n",
       "47     emelecmax fefecuador pueden esperar hijoputa n...\n",
       "48     varios días observado sesgo fujimorista optand...\n",
       "                             ...                        \n",
       "946            increíble cantidad bobadas twiteaba ex😂😂😂\n",
       "947             conozco hace poquito tomé cariño solcito\n",
       "948    vale verga gente alguien importa convierto pri...\n",
       "949    gracias queridas amigas rcreadores romanticism...\n",
       "950                                  heyjeans buen viaje\n",
       "951    sirenita 💕🐚🐠 sirena famosa toda posando nosotr...\n",
       "952    diariocorreo do hermanitos hermanita menor car...\n",
       "954    jhueb bradgehlosu troncarternlu exactly right ...\n",
       "955    acordo perfeito httpstcogcgczwtd4z belissima m...\n",
       "959    gabrielitaaa10 rosamariabartra alvarosarco mil...\n",
       "960          tu date cuenta vale va salir toda si quiero\n",
       "961     meyastos salvajedigital pánico sabe cochinaditas\n",
       "962    repost mheremer ・・・ name something hot and wet...\n",
       "964    larryportera idlr puedes adelantar poquito fiz...\n",
       "965    tiowalo9 labandadel86 edad colombia estan viaj...\n",
       "971    kelitagrand2 xileone si pues “tipa” q veas exc...\n",
       "974    acordo perfeito httpstcogcgczwtd4z maisvoce ma...\n",
       "977                    fernandarrocha toda hora nem xuxa\n",
       "978    tubinocarlos ja cómo orinas gente amoral digni...\n",
       "980             dando argumentos solicite asilo político\n",
       "982    ayacucho casi lleva 3 pto huancayo empató 33 v...\n",
       "985    quem diria 20102018 sérgio lobo sessentou ning...\n",
       "987    tiowalo9 labandadel86 tener miedo poner jugado...\n",
       "988    convocape milagrosleivag mulderrctm aún ustede...\n",
       "989    noticiastvperu richardacunan ustedes sarta cor...\n",
       "990                     gente do céu cuzco é muito longe\n",
       "993    xileone luzsalgador tvperupe imagínate q indig...\n",
       "994    abrazo inmenso aquí cielo ¡feliz cumpleaños ab...\n",
       "996    feliz siento orgullosa pertenecer gran familia...\n",
       "997    padre caraquista pedro quiere magallanero pped...\n",
       "Name: Tweets_clean, Length: 523, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_df['Tweets_clean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(523, 12)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1                       exitosape julianaoxenford dice\n",
       "3    ultima noche semanademayordomia iasdesperanza ...\n",
       "6    cooking with fire in peru today cookingclass s...\n",
       "7    show música vivo tecnópolis cierra temporada 2...\n",
       "8                    maldita perra perdón alteré 💁🏻‍♀️\n",
       "Name: Tweets_clean, dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_df['Tweets_clean'] = tweets_df['Tweets_clean'].apply(lambda x: \" \".join([Word(word).lemmatize() for word in x.split()]))\n",
    "print(tweets_df.shape)\n",
    "tweets_df['Tweets_clean'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# displacy.serve(doc, style=\"dep\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" id=\"0\" class=\"displacy\" width=\"350\" height=\"237.0\" style=\"max-width: none; height: 237.0px; color: #000000; background: #ffffff; font-family: Arial\">\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"147.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"50\">exitosape</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"50\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"147.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"150\">julianaoxenford</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"150\">ADJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"147.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"250\">dice</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"250\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-0-0\" stroke-width=\"2px\" d=\"M70,102.0 C70,2.0 250.0,2.0 250.0,102.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-0-0\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">nsubj</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M70,104.0 L62,92.0 78,92.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-0-1\" stroke-width=\"2px\" d=\"M70,102.0 C70,52.0 145.0,52.0 145.0,102.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-0-1\" class=\"displacy-label\" startOffset=\"50%\" fill=\"currentColor\" text-anchor=\"middle\">amod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M145.0,104.0 L153.0,92.0 137.0,92.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "doc = nlp(tweets_df['Tweets_clean'][1])\n",
    "from IPython.display import HTML, Image, display\n",
    "displacy.render(doc, style=\"dep\",jupyter=True,options={'distance':100})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"entities\" style=\"line-height: 2.5\">exitosape julianaoxenford dice</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "displacy.render(doc, style=\"ent\",jupyter=True,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[WordList(['exitosape', 'julianaoxenford']),\n",
       " WordList(['julianaoxenford', 'dice'])]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TextBlob(tweets_df['Tweets_clean'][1]).ngrams(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3630, 4)\n"
     ]
    },
    {
     "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>words</th>\n",
       "      <th>tf</th>\n",
       "      <th>idf</th>\n",
       "      <th>tfidf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dice</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.774675</td>\n",
       "      <td>26.422724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>julianaoxenford</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.259581</td>\n",
       "      <td>6.259581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>exitosape</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.566434</td>\n",
       "      <td>11.132869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>httpstcogmo19ienh6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.259581</td>\n",
       "      <td>6.259581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>iasdesperanza</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.259581</td>\n",
       "      <td>6.259581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>l…</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.551531</td>\n",
       "      <td>3.551531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fiel</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.259581</td>\n",
       "      <td>6.259581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>noche</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.873287</td>\n",
       "      <td>14.619861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>semanademayordomia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.259581</td>\n",
       "      <td>6.259581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>dios</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.650144</td>\n",
       "      <td>13.950431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                words   tf       idf      tfidf\n",
       "0                dice  7.0  3.774675  26.422724\n",
       "1     julianaoxenford  1.0  6.259581   6.259581\n",
       "2           exitosape  2.0  5.566434  11.132869\n",
       "3  httpstcogmo19ienh6  1.0  6.259581   6.259581\n",
       "4       iasdesperanza  1.0  6.259581   6.259581\n",
       "5                  l…  1.0  3.551531   3.551531\n",
       "6                fiel  1.0  6.259581   6.259581\n",
       "7               noche  3.0  4.873287  14.619861\n",
       "8  semanademayordomia  1.0  6.259581   6.259581\n",
       "9                dios  3.0  4.650144  13.950431"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf1 = (tweets_df['Tweets_clean']).apply(lambda x: pd.value_counts(x.split(\" \"))).sum(axis = 0).reset_index()\n",
    "tf1.columns = ['words','tf']\n",
    "for i,word in enumerate(tf1['words']):\n",
    "    tf1.loc[i, 'idf'] = np.log(tweets_df.shape[0]/(len(tweets_df[tweets_df['Tweets_clean'].str.contains(word)])))\n",
    "\n",
    "tf1['tfidf'] = tf1['tf'] * tf1['idf']\n",
    "print(tf1.shape)\n",
    "tf1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop = set(stopwords.words('spanish'))\n",
    "stop |= set(['lima','si','ser'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<523x1000 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 2517 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf = TfidfVectorizer(max_features=1000, lowercase=True, analyzer='word', stop_words= stop,ngram_range=(1,1))\n",
    "train_vect = tfidf.fit_transform(tweets_df['Tweets_clean'])\n",
    "train_vect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(523, 1000)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<523x1000 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 2618 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "bow = CountVectorizer(max_features=1000, lowercase=True, ngram_range=(1,1),analyzer = \"word\")\n",
    "train_bow = bow.fit_transform(tweets_df['Tweets_clean'])\n",
    "print(train_bow.shape)\n",
    "train_bow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "\n",
    "def find_similar(tfidf_matrix, index, top_n = 5):\n",
    "    cosine_similarities = linear_kernel(tfidf_matrix[index:index+1], tfidf_matrix).flatten()\n",
    "    related_docs_indices = [i for i in cosine_similarities.argsort()[::-1] if i != index]\n",
    "    return [(index, cosine_similarities[index]) for index in related_docs_indices][0:top_n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>date</th>\n",
       "      <th>followers</th>\n",
       "      <th>location</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>word_count</th>\n",
       "      <th>char_count</th>\n",
       "      <th>avg_word</th>\n",
       "      <th>hastags</th>\n",
       "      <th>numerics</th>\n",
       "      <th>Tweets_clean</th>\n",
       "      <th>ACTIONS</th>\n",
       "      <th>NOUNS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>2018-10-20 02:32:30</td>\n",
       "      <td>159</td>\n",
       "      <td>Brasil</td>\n",
       "      <td>@RogerioVilela @pauloap O problema é, quantos serão mortos???</td>\n",
       "      <td>7</td>\n",
       "      <td>53</td>\n",
       "      <td>6.714286</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>rogeriovilela pauloap problema é quantos serão mortos</td>\n",
       "      <td>serão</td>\n",
       "      <td>problema, quantos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    date  followers location  \\\n",
       "542  2018-10-20 02:32:30        159   Brasil   \n",
       "\n",
       "                                                            Tweets  \\\n",
       "542  @RogerioVilela @pauloap O problema é, quantos serão mortos???   \n",
       "\n",
       "     word_count  char_count  avg_word  hastags  numerics  \\\n",
       "542           7          53  6.714286        0         0   \n",
       "\n",
       "                                              Tweets_clean ACTIONS  \\\n",
       "542  rogeriovilela pauloap problema é quantos serão mortos   serão   \n",
       "\n",
       "                 NOUNS  \n",
       "542  problema, quantos  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweet = tweets_df.sample(1)\n",
    "tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['@RogerioVilela @pauloap O problema é, quantos serão mortos???']\n"
     ]
    }
   ],
   "source": [
    "print(tweet['Tweets'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweets</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>@sigridbazan Se agranda mi admiración Sigrid. Lo dices porque lo dices.</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.565055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>@exitosape \\nNo sé qué es lo que sucede en #Exitosa que invitan a adefesios como ese seudoanalista politico de apell...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.535451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>Para mí aquí siempre dice \"MI ALMA\"</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                      Tweets  \\\n",
       "845                                                  @sigridbazan Se agranda mi admiración Sigrid. Lo dices porque lo dices.   \n",
       "440                                                                                                                      NaN   \n",
       "452  @exitosape \\nNo sé qué es lo que sucede en #Exitosa que invitan a adefesios como ese seudoanalista politico de apell...   \n",
       "234                                                                                                                      NaN   \n",
       "900                                                                                      Para mí aquí siempre dice \"MI ALMA\"   \n",
       "\n",
       "        score  \n",
       "845       NaN  \n",
       "440  0.565055  \n",
       "452       NaN  \n",
       "234  0.535451  \n",
       "900       NaN  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_colwidth = 120\n",
    "vals = pd.DataFrame()\n",
    "for index, score in find_similar(train_vect, tweet.index[0],top_n = 5):\n",
    "    vals = vals.append(tweets_df.iloc[index:index+1,:])\n",
    "    vals.loc[index,'score'] = score\n",
    "    \n",
    "vals[['Tweets','score']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = nlp('\\n'.join(tweets_df['NOUNS'].dropna()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Noun</th>\n",
       "      <th>Freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>canaln</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pra</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gente</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>amor</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>patas</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>casa</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>franpetrozzi</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>juez</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>seguridad</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>caso</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mama</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>cosas</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>país</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>día</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>vizcarra</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>persona</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>procesion</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>pueblo</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>señor</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>hora</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>tema</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>fútbol</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>fuerza</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>gracias</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>from</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>equipos</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>años</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>veces</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>presidente</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>milibrujita</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>tratamiento</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>tumor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>tatahcomenta</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>reacción</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>voz</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>perdoar</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>tempo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>minhas</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>orejas</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>rodríguez</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>personajes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>made</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>they</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>sentimientos</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>narradores</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>emelecmax</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>ghibellini</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>linares</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>chantaje</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>momentos</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>derecho</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>scattered</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>elcomerciocom</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>panoramaptv</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>niñito</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>página</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>video</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>prisión</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>verdad</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>josé</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Noun  Freq\n",
       "0           canaln    13\n",
       "1              pra     6\n",
       "2            gente     6\n",
       "3             amor     6\n",
       "4            patas     6\n",
       "5             casa     5\n",
       "6     franpetrozzi     5\n",
       "7             juez     5\n",
       "8        seguridad     4\n",
       "9             caso     4\n",
       "10            mama     4\n",
       "11           cosas     4\n",
       "12            país     4\n",
       "13             día     4\n",
       "14        vizcarra     4\n",
       "15         persona     4\n",
       "16       procesion     4\n",
       "17          pueblo     3\n",
       "18           señor     3\n",
       "19            hora     3\n",
       "20            tema     3\n",
       "21          fútbol     3\n",
       "22          fuerza     3\n",
       "23         gracias     3\n",
       "24            from     3\n",
       "25         equipos     3\n",
       "26            años     3\n",
       "27           veces     3\n",
       "28      presidente     3\n",
       "29     milibrujita     3\n",
       "..             ...   ...\n",
       "120    tratamiento     1\n",
       "121          tumor     1\n",
       "122   tatahcomenta     1\n",
       "123       reacción     1\n",
       "124            voz     1\n",
       "125        perdoar     1\n",
       "126          tempo     1\n",
       "127         minhas     1\n",
       "128         orejas     1\n",
       "129      rodríguez     1\n",
       "130     personajes     1\n",
       "131           made     1\n",
       "132           they     1\n",
       "133   sentimientos     1\n",
       "134     narradores     1\n",
       "135      emelecmax     1\n",
       "136     ghibellini     1\n",
       "137        linares     1\n",
       "138       chantaje     1\n",
       "139       momentos     1\n",
       "140        derecho     1\n",
       "141      scattered     1\n",
       "142  elcomerciocom     1\n",
       "143    panoramaptv     1\n",
       "144         niñito     1\n",
       "145         página     1\n",
       "146          video     1\n",
       "147        prisión     1\n",
       "148         verdad     1\n",
       "149           josé     1\n",
       "\n",
       "[150 rows x 2 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visited = {}\n",
    "nouns = []\n",
    "for word in corpus:\n",
    "    if word.pos_.startswith('N') and len(word.string) < 15 and len(word.string) > 2:\n",
    "        token = word.string.strip().lower()\n",
    "        if token in visited:\n",
    "            visited[token] += 1\n",
    "            continue\n",
    "        else:\n",
    "            visited[token] = 1\n",
    "            nouns.append(word)\n",
    "nouns = sorted(nouns, key=lambda w: -visited[w.string.strip().lower()])[:150]\n",
    "pd.DataFrame([[w.text, visited[w.string.strip().lower()]] for w in nouns], columns=['Noun', 'Freq'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):\n",
    "    assert low_dim_embs.shape[0] >= len(labels), \"More labels than embeddings\"\n",
    "    plt.figure(figsize=(18, 18))  # in inches\n",
    "    for i, label in enumerate(labels):\n",
    "        x, y = low_dim_embs[i, :]\n",
    "        plt.scatter(x, y, s=2.0)\n",
    "        plt.annotate(label, xy=(x, y), xytext=(5, 2),\n",
    "                 textcoords='offset points',ha='right',va='bottom')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('snapshot/lima_words_TSNE.png')\n",
    "    plt.show()\n",
    "\n",
    "# Creating the tsne plot [Warning: will take time]\n",
    "tsne = TSNE(perplexity=50.0, n_components=2, init='pca', n_iter=10000)\n",
    "\n",
    "low_dim_embedding = tsne.fit_transform(np.array([word.vector for word in nouns]))\n",
    "\n",
    "# Finally plotting and saving the fig \n",
    "plot_with_labels(low_dim_embedding, [word.text for word in nouns])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>date</th>\n",
       "      <th>followers</th>\n",
       "      <th>location</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>word_count</th>\n",
       "      <th>char_count</th>\n",
       "      <th>avg_word</th>\n",
       "      <th>hastags</th>\n",
       "      <th>numerics</th>\n",
       "      <th>Tweets_clean</th>\n",
       "      <th>ACTIONS</th>\n",
       "      <th>NOUNS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-20 01:57:03</td>\n",
       "      <td>92</td>\n",
       "      <td>Lima</td>\n",
       "      <td>@elpanfletope @AlanGarciaPeru chancho no come chancho</td>\n",
       "      <td>5</td>\n",
       "      <td>48</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>elpanfletope alangarciaperu chancho come chancho</td>\n",
       "      <td>NaN</td>\n",
       "      <td>elpanfletope</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date  followers location  \\\n",
       "0  2018-10-20 01:57:03         92     Lima   \n",
       "\n",
       "                                                  Tweets  word_count  \\\n",
       "0  @elpanfletope @AlanGarciaPeru chancho no come chancho           5   \n",
       "\n",
       "   char_count  avg_word  hastags  numerics  \\\n",
       "0          48       8.8        0         0   \n",
       "\n",
       "                                       Tweets_clean ACTIONS         NOUNS  \n",
       "0  elpanfletope alangarciaperu chancho come chancho     NaN  elpanfletope  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_df = pd.read_csv('tweets_solutions.csv')\n",
    "tweets_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['hashtags', 'hashtags_flat', 'hashtag_counts', 'hashtag_freq', 'top_hashtags', 'overview'])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hashtag_summary = adv.extract_hashtags(tweets_df['Tweets'])\n",
    "hashtag_summary.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'num_posts': 1000,\n",
       " 'num_hashtags': 344,\n",
       " 'hashtags_per_post': 0.344,\n",
       " 'unique_hashtags': 238}"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hashtag_summary['overview']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[],\n",
       " [],\n",
       " [],\n",
       " ['#semanademayordomia'],\n",
       " [],\n",
       " [],\n",
       " ['#cookingclass', '#seerundo', '#lima', '#peru', '#southamerica'],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " ['#eslae'],\n",
       " [],\n",
       " ['#comprometidosconlaverdad', '#forensesec'],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " []]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hashtag_summary['hashtags'][:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 1, 0, 0, 5, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hashtag_summary['hashtag_counts'][:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 841),\n",
       " (1, 93),\n",
       " (2, 30),\n",
       " (3, 7),\n",
       " (4, 6),\n",
       " (5, 5),\n",
       " (6, 10),\n",
       " (7, 5),\n",
       " (8, 1),\n",
       " (9, 2)]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hashtag_summary['hashtag_freq'][:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgjI+7B/ittWvXasOGDTp58qRGjx6tiIgId48EAACAcuTyPZ/jx49Xy5YtFRYWVmL5pk2bFBwcrC5dumj+/PmSpHvvvVdz587VggULtGrVKlePBgAAAMNcHp/Dhg1TSkpKiWVFRUWaOnWqUlJSlJaWptTUVO3fv99x/6uvvqrRo0e7ejQAAAAY5vL4DAkJUZ06dUosS09PV0BAgAICAuTr66vo6GitW7dOdrtdzz//vHr37q327du7ejQAAAAY5pZjPnNyctSwYUPHbX9/f+3atUuLFy/W559/rry8PGVkZGjkyJGXPTcpKUlJSUmSpOjoaMXExJgaW91vL3bZupveZJdU/uvPzc0t93WidHl5ee4eAb8T29A7sB29A9vRs/j5+V3T49wSn3a7/bJlFotFcXFxiouLu+pzY2NjFRsb66rRrmrb8UwXrr1Y246X/47oa/1FQPnhZ+752Ibege3oHdiO3sctp1ry9/fX0aNHHbezs7P55QIAALgBuCU+O3bsqIyMDGVmZurixYtatWqV+vXr545RAAAAYJDLP3aPi4tTWlqaTp06pXbt2mnKlCkaPny4ZsyYoSFDhqi4uFh/+MMf1KJFC1ePgmtw9+wd7h6hzL6Z3M3dIwAAgGvk8vhMTEy84vLIyEhFRka6+uUBAABQgXjk5TVtNpvi4+Nls9ncPQoAAADKoEJdXvNaWa1WWa1Wd48BAACAMvLIPZ8AAADwTMQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABjjkfHJqZYAAAA8E6daAgAAgDEeuecTAAAAnon4BAAAgDHEJwAAAIwhPgEAAGAM8QkAAABjiE8AAAAY45HxyXk+AQAAPBPn+QQAAIAxHrnnEwAAAJ6J+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGOM0Pj/66CPl5+dLkubMmaORI0fq22+/dflgAAAA8D5O4/PVV19VrVq1tH37dn322WcaOnSopkyZYmK2UnGSeQAAAM/k9CTzlSr92qcbNmzQqFGj1L9/f82aNcvlg10NJ5kHAADwTE73fDZo0ECTJk3S6tWr1adPH/3yyy+y2+0mZgMAAICXcRqff//73xUREaGUlBTdcsstOn36tJ577jkTswEAAMDLOI3PyZMna+DAgWratKkkyc/PTytWrHD5YAAAAPA+TuNz3759JW4XFRXxbXcAAABcl1K/cDRv3jzNmzdPFy5cUOPGjR3Hefr6+mrEiBHGBgQAAID3KDU+J06cqIkTJ+qFF17QtGnTTM4EAAAAL+X0Y/enn35aK1as0Jw5cyRJR48eVXp6ussHAwAAgPdxGp8JCQn6+uuvtXLlSklSzZo1lZCQ4PLBAAAA4H2cxueuXbs0a9YsVa1aVZJUu3ZtFRYWunywq+EKRwAAAJ7J6RWOqlSpoqKiIlksFknSyZMnHX92F65wBAAA4Jmc7vkcM2aM/vSnP+nEiRN66aWXNHDgQE2cONHEbAAAAPAyTvd8xsTEqH379vriiy9kt9uVlJSkoKAgE7MBAADAyzjd8ylJP/30k6pXr66//OUvuvXWW5WZmenquQAAAOCFnMbnK6+8ogULFmj+/PmSpEuXLmncuHEuHwwAAADex2l8rlmzRsnJyapRo4akX6/tXlBQ4PLBAAAA4H2cxqevr68sFovjG+6EJwAAAK6X0/gcNGiQJk2apLy8PC1dulQxMTEaPny4idkAAADgZZx+2/2RRx7Rli1bdNNNN+nAgQNKSEhQeHi4gdEAAADgbZzG57JlyxQcHKzp06cbGAcAAADezGl8Hj58WP/4xz905MgRtWvXTsHBwQoODlbbtm1NzAcAAAAv4jQ+p06dKkk6f/68li5dqoULF+qZZ57RsWPHXD4cAAAAvIvT+JwzZ4527typgoICtW3bVtOnT1dwcLCJ2Upls9lks9m4xjsAAICHcRqfa9askY+PjyIjIxUSEqLOnTurWrVqJmYrFdEJAADgmZyeamnz5s368MMP1bFjR23ZskX33HOPBgwYYGI2AAAAeBmnez737t2r7du3a+vWrdq9e7caNmzo9o/dAQAA4Jmcxufzzz+vkJAQjRkzRh07dlSVKlVMzAUAAAAv5PRj9549e+qxxx5T165dHeH55ptvunwwAAAAeB+n8fnBBx9ctiwlJcUlwwAAAMC7lfqx+4cffqiVK1cqKyurxLXc8/PzdeuttxoZDgAAAN6l1Pjs0qWL6tevr1OnTmncuHGO5bVq1VLr1q2NDAcAAADvUmp83nnnnbrzzju1bt06k/MAAADAizk95hMAAAAoL8QnAAAAjCk1Ph944AFJv57nEwAAACgPpR7zeezYMaWlpWn9+vWKjo6W3W4vcX/79u1dPhwAAAC8S6nxmZCQoNdee03Z2dl69tlnS9xnsViUmprq8uEAAADgXUqNz6ioKEVFRWnOnDmaNGmSyZkAAADgpZxe233SpElav369tm3bJkkKDQ1V3759XT4YAAAAvI/T+HzhhRf0z3/+Uw8++KAkKTExUTt27NC0adNcPlxpbDabbDabrFarrFar2+aAZ7p79g6Xrbv77cXadjyz3Nf7zeRu5b5OAADcwWl8bty4UZ999pkqVfr1i/HDhg1Tr1693BqfRCcAAIBnuqbzfJ45c8bx57y8PJcNAwAAAO/mdM/nhAkT1KtXL4WGhsput2v79u165plnTMwGAAAAL+M0Ph944AGFhIRo9+7dstvtevbZZ1W/fn0TswEAAMDLOI1PSfLz81O/fv1cPQsAAAC8HNd2BwAAgDHEJwAAAIy5anwWFxcrLCzM1CwAAADwcleNz0qVKql169Y6cuSIqXkAAADgxZx+4ejYsWPq0aOHOnXqpBo1ajiWJycnu3QwAAAAeB+n8Tl58mQTcwAAAOAG4DQ+Q0NDdfjwYR06dEg9e/bUuXPnVFxcbGI2AAAAeBmn33ZfunSpRo8e7dgDmpOTo9jYWJcPBgAAAO/jND7feustrVmzRrVq1ZIkNW3aVCdPnnT5YAAAAPA+TuOzatWq8vX1ddy+dOmSLBaLS4cCAACAd3J6zGdISIjmzp2rCxcuaMuWLXrnnXfUt29fE7MBAADAyzjd8zlt2jTVrVtXLVu21JIlS9SnTx899dRTJmYDAACAl3G657NSpUoaOnSoOnXqJIvFosDAQD52BwAAwHVxGp+ffvqpnnjiCQUEBMhutysrK0uzZ89Wnz59TMwHAAAAL+I0Pp977jmlpqaqSZMmkqSMjAw99NBDxCcAAADKzOkxn3Xr1nWEpyQFBASobt26Lh0KAAAA3qnUPZ+ffPKJJKlFixYaNmyYBg0aJIvFotWrV6tjx47GBgQAAID3KDU+bTab48/16tXT1q1bJUm33Xabfv75Z9dPBgAAAK9TanwuWLDA5BxlYrPZZLPZZLVaZbVa3T0OAAAArpHTLxxlZmbq73//uw4fPqxLly45licnJ7t0sKshOgEAADyT0/j805/+pIceekhWq5XzewIAAOB3cRqfVatWVVxcnIlZAAAA4OWcxmdcXJxeeeUVhYeHy9fX17G8ffv2Lh0MAAAA3sdpfP7rX//SihUr9OWXX6pSpV9PC2qxWJSamury4QAAAOBdnMbn2rVr9c0335TY6wkAAABcD6dXOGrdurXOnDljYhYAAAB4Oad7Pk+cOKGQkBB16NBBVatWdSx356mWAAAA4JmcxueUKVNMzAEAAIAbgNP4DA0NNTEHAAAAbgBO4zMgIMBxcvnCwkIVFhaqRo0aysjIcPlwAAAA8C5O4/PHH38scXvt2rVKT0931TwAAADwYk6/7f6f7r33Xn311VeumAUAAABezumez08++cTx5+LiYu3evZtrvAMAAOC6OI1Pm832/w/28dGdd96ppKQklw4FAAAA7+Q0PhcsWGBiDgAAANwASo3P2bNnl/oki8WiSZMmuWQgAAAAeK9S47NGjRqXLTt37pyWLVumU6dOEZ8AAAAos1Lj8+GHH3b8OT8/X2+++aaWL1+u+++/X+PGjTMyHAAAALzLVY/5PH36tN544w2tXLlSQ4cO1aZNm1S7dm1TswEAAMDLlBqf06dP15o1azRixAh9/vnnqlWrlsm5AAAA4IVKjc/XX39dVatW1auvvqq5c+c6ltvtdlksFi6vCQAAgDIrNT6PHz9ucg4AAADcAMp8eU0AAADgehGfAAAAMIb4BAAAgDHEJwAAAIwhPgEAAGAM8QkAAABjiE8AAAAYQ3wCAADAGOITAAAAxhCfAAAAMIb4BAAAgDHEJwAAAIwhPgEAAGBMhYnPH3/8URMmTNCoUaPcPQoAAABcxKXxOX78eLVs2VJhYWEllm/atEnBwcHq0qWL5s+fL0kKCAhw/BkAAADeyaXxOWzYMKWkpJRYVlRUpKlTpyolJUVpaWlKTU3V/v37XTkGAAAAKgiXxmdISIjq1KlTYll6eroCAgIUEBAgX19fRUdHa926da4cAwAAABWEj+kXzMnJUcOGDR23/f39tWvXLp06dUovv/yyvv/+e82bN08TJ0684vOTkpKUlJQkSYqOjlZMTIyRuSWp++3FLlt305vsksp//bm5uWV6vCvfo6tUpPdYUbYjrl9eXp67R7ihvPRphkvW2/Qmuw6edc26n+7b2CXrxeX4++hZ/Pz8rulxxuPTbrdftsxisejWW2/V7NmznT4/NjZWsbGxrhjNqW3HM1249mJtO17+O6Kv9Rfh31z7Hl2jYr3HirEd8fvw8zbHdX8fXfN3UeL3wzR+3t7H+Lfd/f39dfToUcft7OxsfrEAAABuEMbjs2PHjsrIyFBmZqYuXryoVatWqV+/fqbHAAAAgBu4ND7j4uLUv39/HThwQO3atVNycrJ8fHw0Y8YMDRkyRKGhoYqKilKLFi1cOQYAAAAqCJce85mYmHjF5ZGRkYqMjHTlSwMAAKACqjBXOCoLm82m+Ph42Ww2d48CAACAMjD+bffyYLVaZbVa3T0GAAAAysgj93wCAADAMxGfAAAAMIb4BAAAgDHEJwAAAIwhPgEAAGCMR8Ynp1oCAADwTJxqCQAAAMZ45J5PAAAAeCbiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgjEeeaslms8lms3HKJaAUd8/e4e4Ryuybyd3cPQIAwACPjE+iEwAAwDPxsTsAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADDGI8/zyUnmAbjqRPrdby/WtuOZLlk3J9IHAA+NT6ITAADAM/GxOwAAAIwhPgEAAGAM8QkAAABjiE8AAAAYQ3wCAADAGOITAAAAxhCfAAAAMIb4BAAAgDHEJwAAAIzxyCsccXlNADcCV11C1JW4hOjl2I5ASR4Zn0QnAACAZ+JjdwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGeGR82mw2xcfHy2azuXsUAAAAlIGPuwe4HlarVVar1d1jAAAAoIw8cs8nAAAAPBPxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMR4ZnzabTfHx8bLZbO4eBQAAAGXg4+4BrofVapXVanX3GAAAACgjj9zzCQAAAM9EfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMcQnAAAAjCE+AQAAYAzxCQAAAGOITwAAABhDfAIAAMAY4hMAAADGEJ8AAAAwhvgEAACAMT7uHuB62Gw22Ww2Wa1WWa1Wd48DAABvvqxJAAAOA0lEQVS82N2zd7h7hOvyzeRu7h7hijwyPolOAAAAz8TH7gAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgDPEJAAAAY4hPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACM8XH3AP9WUFCgKVOmyNfXV6GhoYqJiXH3SAAAAChnLt3zOX78eLVs2VJhYWEllm/atEnBwcHq0qWL5s+fL0las2aNoqKiNHfuXK1fv96VYwEAAMBNXBqfw4YNU0pKSollRUVFmjp1qlJSUpSWlqbU1FTt379f2dnZ8vf3lyRVrlzZlWMBAADATVwanyEhIapTp06JZenp6QoICFBAQIB8fX0VHR2tdevWyd/fXzk5OZKk4uJiV44FAAAANzF+zGdOTo4aNmzouO3v769du3ZpzJgxmjp1qjZs2CCr1Vrq85OSkpSUlCRJio6ONnpsaPfbXRfFTW+ySyr/9efm5pbp8a58j65Skd4j2/H6VZT36KptKFWc9+hKFeU9sh1/n7K8x5c+zXDZHE1vsuvgWdes/+m+ja/5sZ64DaWy/67+Xn5+ftf0OOPxabfbL1tmsVhUs2ZNLViwwOnzY2NjFRsb64rRnNp2PNOFay/WtuPlvyP6Wn8R/s2179E1KtZ7ZDter4rzHl2zDaWK9B5dp+K8R7bj71GW9+iJ/6ZKFek9uk5Zf1dNMX6qJX9/fx09etRxOzs7u8L+cAAAAFC+jMdnx44dlZGRoczMTF28eFGrVq1Sv379TI8BAAAAN3BpfMbFxal///46cOCA2rVrp+TkZPn4+GjGjBkaMmSIQkNDFRUVpRYtWrhyDAAAAFQQLj3mMzEx8YrLIyMjFRkZ6cqXBgAAQAXkkZfXtNlsio+Pl81mc/coAAAAKIMKc3nNsrBarVc9HRMAAAAqJo/c8wkAAADPRHwCAADAGOITAAAAxhCfAAAAMIb4BAAAgDEeGZ+cagkAAMAzcaolAAAAGOORez4BAADgmYhPAAAAGEN8AgAAwBjiEwAAAMYQnwAAADDGcuLECbu7h4CUlJSk2NhYd4+B34nt6PnYht6B7egd2I7eiT2fFURSUpK7R0A5YDt6Prahd2A7ege2o3ciPgEAAGAM8QkAAABjKk+ZMmW6u4fAr9q3b+/uEVAO2I6ej23oHdiO3oHt6H34whEAAACM4WN3AAAAGEN8utmmTZsUHBysLl26aP78+e4eB9fh6NGjio6OVkhIiHr06KE333zT3SPhdygqKlJERIQeeughd4+C63TmzBmNGjVK3bt3V0hIiL7++mt3j4TrsGjRIvXo0UNhYWGKi4vThQsX3D0Sygnx6UZFRUWaOnWqUlJSlJaWptTUVO3fv9/dY6GMKleurP/6r//S1q1btX79er399ttsRw+WmJiooKAgd4+B3+Gpp55Sr169tG3bNm3ZsoXt6YFycnK0ePFibdiwQV9++aWKioqUmprq7rFQTohPN0pPT1dAQIACAgLk6+ur6OhorVu3zt1joYz8/PwcB8TXqlVLQUFBysnJcfNUuB7Z2dnasGGDhg8f7u5RcJ3Onj2r7du3O7ahr6+vbrnlFjdPhetx6dIlXbhwQZcuXdL58+fl5+fn7pFQTohPN8rJyVHDhg0dt/39/YkWD5eVlaXvvvtOnTt3dvcouA5PP/20nnvuOVWqxD+NnurHH3/Ubbfdpscee0wRERGaOHGiCgoK3D0WyqhBgwZ6+OGH1aFDB7Vp00Y333yzIiIi3D0Wygn/wrqR3X75iQYsFosbJkF5yM/P16hRo/Tiiy/qpptucvc4KKNPP/1U9erV47QuHq6oqEh79uzRqFGj9Nlnn6lGjRp67bXX3D0Wyujnn3/W+vXrtWvXLn333Xc6d+6cVqxY4e6xUE6ITzfy9/fX0aNHHbezs7P5WMFDFRYWatSoUYqJidHAgQPdPQ6uw44dO7R+/Xp16tRJY8aM0VdffaVx48a5eyyUUYMGDeTv7+/49OG+++7Tnj173DwVyurzzz/XXXfdpbp166pKlSoaMGAAXxzzIsSnG3Xs2FEZGRnKzMzUxYsXtWrVKvXr18/dY6GM7Ha7Jk6cqKCgIGLFg02bNk179uxRenq6Fi9erB49euiNN95w91goo/r168vf318HDhyQJH355Zdq3ry5m6dCWd1xxx3atWuXzp07J7vdri+++ELNmjVz91goJz7uHuBG5uPjoxkzZmjIkCEqLi7WH/7wB7Vo0cLdY6GMduzYoQ8++ECtWrVSeHi4pF+PHYyMjHTvYMANasaMGRo7dqwKCwvVqFEjPnb3QJ07d9Z9992n3r17y8fHR23btlVsbKy7x0I54QpHAAAAMIaP3QEAAGAM8QkAAABjiE8AAAAYQ3wCAADAGOITAAAAxhCfADxeo0aNStxevny5EhISyryetLQ0PfTQQ1e8b9GiRTp37tx1zecOaWlp2rlzp8vWn5WVpZUrV7ps/QC8F/EJANcgMTFR58+fd/cYpbp06VKJ22lpaS69Iszhw4eJTwDXhfgE4NVsNpusVqsiIiL04IMP6vjx45J+jbPw8HCFh4crIiJC+fn5kqSCggKNGjVK3bt319ixY2W325WYmKjc3Fzdf//9io6OliQ98cQT6tOnj3r06KGZM2c6Xm/Dhg3q3r27BgwYoCeffNKxJ7W01/u3rKwsde/eXY888oh69uypUaNGOfa0fvvtt4qKilLv3r01ePBg5ebmSpIGDRqkF198UVFRUUpMTCyxrnfffVeLFi1SeHi40tLSdPfdd8tut+vMmTO6/fbbtXXrVknSwIEDdejQIRUUFGj8+PGKjIxURESE1q1bJ+nXa6VPnz5dkZGR6tmzp5YsWSJJeuGFF7R9+3aFh4dr0aJF5bvRAHg1rnAEwONduHDBcXUpSfr5559ltVolSd26ddP69etlsVi0dOlS/fd//7eef/55vf7665o5c6a6deum/Px8VatWTZL03Xff6auvvpKfn58GDBigHTt2KC4uTosWLVJqaqpuu+02SdJTTz2lOnXqqKioSA888ID+53/+R02bNtXkyZO1evVqNWrUSHFxcY6ZSnu93zpw4IDmzZunbt26afz48XrnnXcUFxenJ598UklJSapbt65SU1P18ssvO67ak5eXp9WrV5dYz1133aWRI0eqZs2aeuSRRyRJTZo00f79+5WVlaX27dtr+/bt6ty5s3JyctSkSRO9+OKLCgsL02uvvaYzZ86ob9++uueee7Ry5UrddNNN2rBhg3755RcNGDBA4eHhmjZtmhYuXKj33nuv/DYkgBsC8QnA41WrVk1btmxx3F6+fLl2794tScrOztaYMWN07NgxXbx4UXfddZckqWvXrnr22Wf14IMPauDAgapVq5YkqVOnTvL395cktWnTRocPH1ZwcPBlr/nRRx8pKSlJRUVFOnbsmH744QcVFxerUaNGjmNQH3jgASUlJV319X6rYcOG6tatmyRp8ODBWrx4sXr16qW9e/cqJiZGklRcXKz69es7njNo0KBr+hkFBwdr27ZtysrK0oQJE7R06VKFhISoQ4cOkqQtW7bIZrNp4cKFkqRffvlFR48e1WeffaZ//etf+vjjjyVJZ8+e1aFDh+Tr63tNrwsA/4n4BODVnnzySY0bN079+vVTWlqaZs2aJUmaMGGCIiMjtXHjRvXr189x/OJvo6pSpUqXHUspSZmZmVq4cKE2bNig2rVr69FHH9WFCxdkt5d+teIrvV6zZs1KPMZisVx22263q0WLFo6Pwf9TzZo1r+nnEBwcrCVLlig3N1cJCQlauHCh0tLS1L17d0mS3W7XO++8o8DAwBLPs9vtmjFjhnr16lVieVpa2jW9LgD8J475BODV8vLy1KBBA0lSSkqKY3lGRoZatWql8ePHq0OHDvrf//3fq66nVq1ajuM0z549q5o1a+rmm2/W8ePHtXnzZklSs2bNlJmZqaysLEnSqlWryvR6R44ccXxJ6MMPP1S3bt0UGBiokydPOpYXFhZq3759Tt/3b+eVpM6dO+vrr79WpUqVVK1aNbVp00ZJSUmOvboRERFavHixI6D37NkjSerVq5feffddFRYWSpIOHjyogoKCy9YPANeK+ATg1aZMmaI///nPGjhwoON4TUl68803FRYWpvDwcFWrVk29e/e+6npGjBihYcOGKTo6Wm3atFGbNm3Uo0cPTZgwQV27dpUkVa9eXbNmzdLQoUM1YMAA1atXTzfffPM1v15QUJDef/999ezZU6dPn9bIkSPl6+urt99+W88//7zjy0rX8i12q9WqtWvXKjw8XNu2bVPVqlXl7++vzp07S/p1T2h+fr5atWolSZo0aZIuXbqknj17KiwsTH/7298kScOHD1dQUJB69+6tsLAwTZo0SUVFRWrVqpV8fHz4whGAMrOcOHGi9M+JAABlkp+fr1q1aslutyshIUFNmjTR2LFjnT4vKytLf/zjH/Xll18amBIA3Ic9nwBQjpKTkxUeHq4ePXooLy9PsbGx7h4JACoU9nwCAADAGPZ8AgAAwBjiEwAAAMYQnwAAADCG+AQAAIAxxCcAAACMIT4BAABgzP8BDjE61r1lR/AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(facecolor='#ebebeb', figsize=(11, 8))\n",
    "plt.bar([x[0] for x in hashtag_summary['hashtag_freq'][:15]],\n",
    "        [x[1] for x in hashtag_summary['hashtag_freq'][:15]])\n",
    "plt.title('Hashtag frequency')\n",
    "plt.xlabel('Hashtags per tweet')\n",
    "plt.ylabel('Number of tweets')\n",
    "plt.yscale('log')\n",
    "plt.grid(alpha=0.5)\n",
    "plt.gca().set_frame_on(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('#lima', 9),\n",
       " ('#mulderrctm', 9),\n",
       " ('#22díasparaveracaché', 6),\n",
       " ('#cristomoreno', 6),\n",
       " ('#señordelosmilagros', 6),\n",
       " ('#elorigendelorigen', 5),\n",
       " ('#esviernesyyonecesito', 5),\n",
       " ('#mesmorado', 5),\n",
       " ('#turrondedoñapepa', 5),\n",
       " ('#anticuchos', 5)]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hashtag_summary['top_hashtags'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(facecolor='#ebebeb', figsize=(8, 12))\n",
    "plt.barh([x[0] for x in hashtag_summary['top_hashtags'][2:][:5]][::-1],\n",
    "         [x[1] for x in hashtag_summary['top_hashtags'][2:][:5]][::-1])\n",
    "plt.title('Top Hashtags')\n",
    "# plt.xticks(range(3))\n",
    "plt.grid(alpha=0.5)\n",
    "plt.gca().set_frame_on(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['emoji', 'emoji_text', 'emoji_flat', 'emoji_flat_text', 'emoji_counts', 'emoji_freq', 'top_emoji', 'top_emoji_text', 'overview'])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emoji_summary = adv.extract_emoji(tweets_df['Tweets'])\n",
    "emoji_summary.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'num_posts': 1000,\n",
       " 'num_emoji': 511,\n",
       " 'emoji_per_post': 0.511,\n",
       " 'unique_emoji': 132}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emoji_summary['overview']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['📻', '📣'],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " ['🤔'],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " ['😦'],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " ['😀'],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " []]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emoji_summary['emoji'][50:80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['radio', 'megaphone'],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " ['thinking face'],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " ['frowning face with open mouth'],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " ['grinning face'],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " []]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emoji_summary['emoji_text'][50:80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['💁🏻\\u200d♀️', '🇪🇨', '🇪🇨', '🇪🇨', '🇪🇨', '🤔', '😆', '😆', '😆', '😆']"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emoji_summary['emoji_flat'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['woman tipping hand light skin tone',\n",
       " 'Ecuador',\n",
       " 'Ecuador',\n",
       " 'Ecuador',\n",
       " 'Ecuador',\n",
       " 'thinking face',\n",
       " 'grinning squinting face',\n",
       " 'grinning squinting face',\n",
       " 'grinning squinting face',\n",
       " 'grinning squinting face']"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emoji_summary['emoji_flat_text'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('💁🏻\\u200d♀️', 'woman tipping hand light skin tone'),\n",
       " ('🇪🇨', 'Ecuador'),\n",
       " ('🇪🇨', 'Ecuador'),\n",
       " ('🇪🇨', 'Ecuador'),\n",
       " ('🇪🇨', 'Ecuador'),\n",
       " ('🤔', 'thinking face'),\n",
       " ('😆', 'grinning squinting face'),\n",
       " ('😆', 'grinning squinting face'),\n",
       " ('😆', 'grinning squinting face'),\n",
       " ('😆', 'grinning squinting face')]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(emoji_summary['emoji_flat'][:10], emoji_summary['emoji_flat_text'][:10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 4, 0, 0, 0, 1]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emoji_summary['emoji_counts'][:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 808),\n",
       " (1, 108),\n",
       " (2, 27),\n",
       " (3, 23),\n",
       " (4, 11),\n",
       " (5, 9),\n",
       " (6, 2),\n",
       " (8, 1),\n",
       " (9, 2),\n",
       " (10, 1),\n",
       " (11, 2),\n",
       " (13, 1),\n",
       " (15, 1),\n",
       " (18, 1),\n",
       " (23, 1)]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emoji_summary['emoji_freq'][:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(facecolor='#ebebeb', figsize=(8, 8))\n",
    "plt.bar([x[0] for x in emoji_summary['emoji_freq'][:15]],\n",
    "        [x[1] for x in emoji_summary['emoji_freq'][:15]])\n",
    "plt.title('Emoji frequency')\n",
    "plt.xlabel('Emoji per tweet')\n",
    "plt.ylabel('Number of tweets')\n",
    "plt.yscale('log')\n",
    "plt.grid(alpha=0.5)\n",
    "plt.gca().set_frame_on(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('😭', 39),\n",
       " ('😂', 37),\n",
       " ('🤣', 32),\n",
       " ('❤', 21),\n",
       " ('♥', 20),\n",
       " ('😍', 17),\n",
       " ('🤤', 14),\n",
       " ('😻', 14)]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emoji_summary['top_emoji'][:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('loudly crying face', 39),\n",
       " ('face with tears of joy', 37),\n",
       " ('rolling on the floor laughing', 32),\n",
       " ('red heart', 21),\n",
       " ('heart suit', 20),\n",
       " ('smiling face with heart-eyes', 17),\n",
       " ('drooling face', 14),\n",
       " ('smiling cat face with heart-eyes', 14)]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emoji_summary['top_emoji_text'][:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(facecolor='#ebebeb', figsize=(8, 8))\n",
    "plt.barh([x[0] for x in emoji_summary['top_emoji_text'][:8]][::-1],\n",
    "         [x[1] for x in emoji_summary['top_emoji_text'][:8]][::-1])\n",
    "plt.title('Top Emoji')\n",
    "plt.grid(alpha=0.5)\n",
    "plt.gca().set_frame_on(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['mentions', 'mentions_flat', 'mention_counts', 'mention_freq', 'top_mentions', 'overview'])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mention_summary = adv.extract_mentions(tweets_df.Tweets)\n",
    "mention_summary.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'num_posts': 1000,\n",
       " 'num_mentions': 886,\n",
       " 'mentions_per_post': 0.886,\n",
       " 'unique_mentions': 503}"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mention_summary['overview']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['@elpanfletope', '@alangarciaperu'],\n",
       " ['@exitosape', '@julianaoxenford'],\n",
       " ['@anajuliachs'],\n",
       " ['@iasdesperanza'],\n",
       " ['@fernando_roman1'],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " [],\n",
       " ['@mininterperu', '@pcmperu', '@vizcarrhagan'],\n",
       " [],\n",
       " ['@sasha71396634',\n",
       "  '@ernesto_jx',\n",
       "  '@hninurta',\n",
       "  '@gabospeed94',\n",
       "  '@karla_ugaz',\n",
       "  '@vero_mendoza_f'],\n",
       " [],\n",
       " [],\n",
       " []]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mention_summary['mentions'][:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['@elpanfletope',\n",
       " '@alangarciaperu',\n",
       " '@exitosape',\n",
       " '@julianaoxenford',\n",
       " '@anajuliachs',\n",
       " '@iasdesperanza',\n",
       " '@fernando_roman1',\n",
       " '@mininterperu',\n",
       " '@pcmperu',\n",
       " '@vizcarrhagan']"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mention_summary['mentions_flat'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 2, 1, 1, 1, 0, 0, 0, 0, 3, 0, 6, 0, 0, 0, 1, 1, 1, 0, 0]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mention_summary['mention_counts'][:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 484),\n",
       " (1, 341),\n",
       " (2, 105),\n",
       " (3, 29),\n",
       " (4, 11),\n",
       " (5, 6),\n",
       " (6, 4),\n",
       " (7, 10),\n",
       " (8, 10)]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mention_summary['mention_freq'][:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(facecolor='#ebebeb', figsize=(8, 8))\n",
    "plt.bar([x[0] for x in mention_summary['mention_freq'][:15]],\n",
    "        [x[1] for x in mention_summary['mention_freq'][:15]])\n",
    "plt.title('Mention frequency')\n",
    "plt.xlabel('Mention per tweet')\n",
    "plt.ylabel('Number of tweets')\n",
    "plt.grid(alpha=0.5)\n",
    "plt.yscale('log')\n",
    "plt.gca().set_frame_on(False)\n",
    "plt.savefig('snapshot/Mention Frequency.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('@canaln_', 31),\n",
       " ('@franpetrozzi', 11),\n",
       " ('@idl_r', 10),\n",
       " ('@rppnoticias', 10),\n",
       " ('@rosamariabartra', 9),\n",
       " ('@keikofujimori', 8),\n",
       " ('@ximena_casanova', 7),\n",
       " ('@policiaperu', 7),\n",
       " ('@milagrosleivag', 7),\n",
       " ('@abbu25', 7)]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mention_summary['top_mentions'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(facecolor='#ebebeb', figsize=(8, 8))\n",
    "plt.barh([x[0] for x in mention_summary['top_mentions'][:15]][::-1],\n",
    "         [x[1] for x in mention_summary['top_mentions'][:15]][::-1])\n",
    "plt.title('Top Mentions')\n",
    "plt.grid(alpha=0.5)\n",
    "plt.gca().set_frame_on(False)\n",
    "plt.savefig('snapshot/Top Mentions.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'followers', 'location', 'Tweets', 'word_count', 'char_count',\n",
       "       'avg_word', 'hastags', 'numerics', 'Tweets_clean', 'ACTIONS', 'NOUNS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweets</th>\n",
       "      <th>followers</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>hashcounts</th>\n",
       "      <th>mentions</th>\n",
       "      <th>mention_count</th>\n",
       "      <th>emoji</th>\n",
       "      <th>emoji_text</th>\n",
       "      <th>emoji_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@elpanfletope @AlanGarciaPeru chancho no come chancho</td>\n",
       "      <td>92</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[@elpanfletope, @alangarciaperu]</td>\n",
       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@exitosape @JulianaOxenford A DONDE DICE ...</td>\n",
       "      <td>32</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[@exitosape, @julianaoxenford]</td>\n",
       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@anajuliachs kkkk 1 mês</td>\n",
       "      <td>195</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[@anajuliachs]</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ultima noche de #SemanaDeMayordomia en la @IASDEsperanza, aprendiendo sobre la importancia de ser fiel a Dios con l…...</td>\n",
       "      <td>228</td>\n",
       "      <td>[#semanademayordomia]</td>\n",
       "      <td>1</td>\n",
       "      <td>[@iasdesperanza]</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@fernando_roman1 Jajaja</td>\n",
       "      <td>123</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[@fernando_roman1]</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                    Tweets  \\\n",
       "0                                                                    @elpanfletope @AlanGarciaPeru chancho no come chancho   \n",
       "1                                                                             @exitosape @JulianaOxenford A DONDE DICE ...   \n",
       "2                                                                                                  @anajuliachs kkkk 1 mês   \n",
       "3  Ultima noche de #SemanaDeMayordomia en la @IASDEsperanza, aprendiendo sobre la importancia de ser fiel a Dios con l…...   \n",
       "4                                                                                                  @fernando_roman1 Jajaja   \n",
       "\n",
       "   followers               hashtags  hashcounts  \\\n",
       "0         92                     []           0   \n",
       "1         32                     []           0   \n",
       "2        195                     []           0   \n",
       "3        228  [#semanademayordomia]           1   \n",
       "4        123                     []           0   \n",
       "\n",
       "                           mentions  mention_count emoji emoji_text  \\\n",
       "0  [@elpanfletope, @alangarciaperu]              2    []         []   \n",
       "1    [@exitosape, @julianaoxenford]              2    []         []   \n",
       "2                    [@anajuliachs]              1    []         []   \n",
       "3                  [@iasdesperanza]              1    []         []   \n",
       "4                [@fernando_roman1]              1    []         []   \n",
       "\n",
       "   emoji_count  \n",
       "0            0  \n",
       "1            0  \n",
       "2            0  \n",
       "3            0  \n",
       "4            0  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_tweets =  (tweets_df[['Tweets', 'followers']]\n",
    " .assign(hashtags=hashtag_summary['hashtags'],\n",
    "         hashcounts=hashtag_summary['hashtag_counts'],\n",
    "         mentions=mention_summary['mentions'],\n",
    "         mention_count=mention_summary['mention_counts'],\n",
    "         emoji=emoji_summary['emoji'],\n",
    "         emoji_text=emoji_summary['emoji_text'],\n",
    "         emoji_count=emoji_summary['emoji_counts'],))\n",
    "extracted_tweets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Tweets', 'followers', 'hashtags', 'hashcounts', 'mentions',\n",
       "       'mention_count', 'emoji', 'emoji_text', 'emoji_count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_tweets.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>word</th>\n",
       "      <th>abs_freq</th>\n",
       "      <th>wtd_freq</th>\n",
       "      <th>rel_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#lima</td>\n",
       "      <td>9</td>\n",
       "      <td>430823</td>\n",
       "      <td>47869.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>#drogas</td>\n",
       "      <td>2</td>\n",
       "      <td>295632</td>\n",
       "      <td>147816.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>#microcomercializar</td>\n",
       "      <td>2</td>\n",
       "      <td>295632</td>\n",
       "      <td>147816.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>#chorrillos</td>\n",
       "      <td>1</td>\n",
       "      <td>147819</td>\n",
       "      <td>147819.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>#esviernesyyonecesito</td>\n",
       "      <td>5</td>\n",
       "      <td>147727</td>\n",
       "      <td>29545.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>#renuncia</td>\n",
       "      <td>1</td>\n",
       "      <td>107446</td>\n",
       "      <td>107446.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>#pérez</td>\n",
       "      <td>1</td>\n",
       "      <td>107446</td>\n",
       "      <td>107446.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>#tablitasexcel</td>\n",
       "      <td>2</td>\n",
       "      <td>92850</td>\n",
       "      <td>46425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>#22díasparaveracaché</td>\n",
       "      <td>6</td>\n",
       "      <td>40669</td>\n",
       "      <td>6778.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>#burnthestagethemovieinvzla</td>\n",
       "      <td>1</td>\n",
       "      <td>39846</td>\n",
       "      <td>39846.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          word  abs_freq  wtd_freq  rel_value\n",
       "0                        #lima         9    430823    47869.0\n",
       "1                      #drogas         2    295632   147816.0\n",
       "2          #microcomercializar         2    295632   147816.0\n",
       "3                  #chorrillos         1    147819   147819.0\n",
       "4        #esviernesyyonecesito         5    147727    29545.0\n",
       "5                    #renuncia         1    107446   107446.0\n",
       "6                       #pérez         1    107446   107446.0\n",
       "7               #tablitasexcel         2     92850    46425.0\n",
       "8         #22díasparaveracaché         6     40669     6778.0\n",
       "9  #burnthestagethemovieinvzla         1     39846    39846.0"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_freq_hash = adv.word_frequency(extracted_tweets['hashtags'].str.join(' '), \n",
    "                                    extracted_tweets['followers'].fillna(0))#.sort_values(['abs_freq'], ascending=False).head(20)\n",
    "word_freq_hash.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweets</th>\n",
       "      <th>followers</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>hashcounts</th>\n",
       "      <th>mentions</th>\n",
       "      <th>mention_count</th>\n",
       "      <th>emoji</th>\n",
       "      <th>emoji_text</th>\n",
       "      <th>emoji_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cooking with fire in Peru today #cookingclass #seerundo #lima #peru #southamerica @ Lima, Peru https://t.co/llmzIMjMUo</td>\n",
       "      <td>13</td>\n",
       "      <td>[#cookingclass, #seerundo, #lima, #peru, #southamerica]</td>\n",
       "      <td>5</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Shopping for our cooking class today #mercado #seerundo #lima #peru #southamerica @ Mercado N#1 de Surquillo https:/...</td>\n",
       "      <td>13</td>\n",
       "      <td>[#mercado, #seerundo, #lima, #peru, #southamerica, #1]</td>\n",
       "      <td>6</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>Feels good to be back home 🇵🇪 #peru #larcomar #lima @ Lima, Peru https://t.co/agFso0gRcs</td>\n",
       "      <td>247</td>\n",
       "      <td>[#peru, #larcomar, #lima]</td>\n",
       "      <td>3</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[🇵🇪]</td>\n",
       "      <td>[Peru]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>#esviernesyyonecesito es ahora una tendencia en #Lima\\n\\nhttps://t.co/UOmWB9sTSw https://t.co/oZBAUS7GPe</td>\n",
       "      <td>107446</td>\n",
       "      <td>[#esviernesyyonecesito, #lima]</td>\n",
       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>By @mariotestino 🙌🏼 \\n.\\n.\\n.\\n.\\n.\\n.\\n.\\n.\\n#igersperu #lima #museum #musee #peru #mariotestino #photography #mode...</td>\n",
       "      <td>213</td>\n",
       "      <td>[#igersperu, #lima, #museum, #musee, #peru, #mariotestino, #photography, #mode]</td>\n",
       "      <td>8</td>\n",
       "      <td>[@mariotestino]</td>\n",
       "      <td>1</td>\n",
       "      <td>[🙌🏼]</td>\n",
       "      <td>[raising hands medium-light skin tone]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>#pérez es ahora una tendencia en #Lima\\n\\nhttps://t.co/CXgZJw73Ty https://t.co/a7wfyKchQU</td>\n",
       "      <td>107446</td>\n",
       "      <td>[#pérez, #lima]</td>\n",
       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>Pequeña\\n#traveler #explorer #flower #lima #pic #nice #photograpy https://t.co/82r1wgHu3w</td>\n",
       "      <td>553</td>\n",
       "      <td>[#traveler, #explorer, #flower, #lima, #pic, #nice, #photograpy]</td>\n",
       "      <td>7</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>#renuncia es ahora una tendencia en #Lima\\n\\nhttps://t.co/DhxjeOKeix https://t.co/61jYXcNSwk</td>\n",
       "      <td>107446</td>\n",
       "      <td>[#renuncia, #lima]</td>\n",
       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>'cerradura', 'desconocidos' y 'amedrentamiento' es ahora una tendencia en #Lima\\n\\nhttps://t.co/zpEunuSxrb https://t...</td>\n",
       "      <td>107446</td>\n",
       "      <td>[#lima]</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                      Tweets  \\\n",
       "6     Cooking with fire in Peru today #cookingclass #seerundo #lima #peru #southamerica @ Lima, Peru https://t.co/llmzIMjMUo   \n",
       "23   Shopping for our cooking class today #mercado #seerundo #lima #peru #southamerica @ Mercado N#1 de Surquillo https:/...   \n",
       "252                                 Feels good to be back home 🇵🇪 #peru #larcomar #lima @ Lima, Peru https://t.co/agFso0gRcs   \n",
       "330                 #esviernesyyonecesito es ahora una tendencia en #Lima\\n\\nhttps://t.co/UOmWB9sTSw https://t.co/oZBAUS7GPe   \n",
       "389  By @mariotestino 🙌🏼 \\n.\\n.\\n.\\n.\\n.\\n.\\n.\\n.\\n#igersperu #lima #museum #musee #peru #mariotestino #photography #mode...   \n",
       "512                                #pérez es ahora una tendencia en #Lima\\n\\nhttps://t.co/CXgZJw73Ty https://t.co/a7wfyKchQU   \n",
       "653                                Pequeña\\n#traveler #explorer #flower #lima #pic #nice #photograpy https://t.co/82r1wgHu3w   \n",
       "749                             #renuncia es ahora una tendencia en #Lima\\n\\nhttps://t.co/DhxjeOKeix https://t.co/61jYXcNSwk   \n",
       "821  'cerradura', 'desconocidos' y 'amedrentamiento' es ahora una tendencia en #Lima\\n\\nhttps://t.co/zpEunuSxrb https://t...   \n",
       "\n",
       "     followers  \\\n",
       "6           13   \n",
       "23          13   \n",
       "252        247   \n",
       "330     107446   \n",
       "389        213   \n",
       "512     107446   \n",
       "653        553   \n",
       "749     107446   \n",
       "821     107446   \n",
       "\n",
       "                                                                            hashtags  \\\n",
       "6                            [#cookingclass, #seerundo, #lima, #peru, #southamerica]   \n",
       "23                            [#mercado, #seerundo, #lima, #peru, #southamerica, #1]   \n",
       "252                                                        [#peru, #larcomar, #lima]   \n",
       "330                                                   [#esviernesyyonecesito, #lima]   \n",
       "389  [#igersperu, #lima, #museum, #musee, #peru, #mariotestino, #photography, #mode]   \n",
       "512                                                                  [#pérez, #lima]   \n",
       "653                 [#traveler, #explorer, #flower, #lima, #pic, #nice, #photograpy]   \n",
       "749                                                               [#renuncia, #lima]   \n",
       "821                                                                          [#lima]   \n",
       "\n",
       "     hashcounts         mentions  mention_count emoji  \\\n",
       "6             5               []              0    []   \n",
       "23            6               []              0    []   \n",
       "252           3               []              0  [🇵🇪]   \n",
       "330           2               []              0    []   \n",
       "389           8  [@mariotestino]              1  [🙌🏼]   \n",
       "512           2               []              0    []   \n",
       "653           7               []              0    []   \n",
       "749           2               []              0    []   \n",
       "821           1               []              0    []   \n",
       "\n",
       "                                 emoji_text  emoji_count  \n",
       "6                                        []            0  \n",
       "23                                       []            0  \n",
       "252                                  [Peru]            1  \n",
       "330                                      []            0  \n",
       "389  [raising hands medium-light skin tone]            1  \n",
       "512                                      []            0  \n",
       "653                                      []            0  \n",
       "749                                      []            0  \n",
       "821                                      []            0  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_tweets[extracted_tweets['hashtags'].str.join(' ')\n",
    "                  .str.contains('lima',case=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>abs_freq</th>\n",
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       "      <th>0</th>\n",
       "      <td>@gissellereyes</td>\n",
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       "      <td>214460</td>\n",
       "      <td>214460.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@ucatolicaec</td>\n",
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       "      <td>52617</td>\n",
       "      <td>17539.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@macara_oficial</td>\n",
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       "      <td>46425</td>\n",
       "      <td>46425.0</td>\n",
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       "      <td>@dcm_online</td>\n",
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       "      <td>@brasil247</td>\n",
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       "      <td>44161</td>\n",
       "      <td>44161.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>@terranoticiasbr</td>\n",
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       "      <td>44160</td>\n",
       "      <td>44160.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>@canaln_</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>@jacquelinabravo</td>\n",
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       "      <td>31299</td>\n",
       "      <td>31299.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>@alokadalis</td>\n",
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       "      <td>27305</td>\n",
       "      <td>13652.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>@colrichardkemp</td>\n",
       "      <td>1</td>\n",
       "      <td>23611</td>\n",
       "      <td>23611.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               word  abs_freq  wtd_freq  rel_value\n",
       "0    @gissellereyes         1    214460   214460.0\n",
       "1      @ucatolicaec         3     52617    17539.0\n",
       "2   @macara_oficial         1     46425    46425.0\n",
       "3       @dcm_online         1     44161    44161.0\n",
       "4        @brasil247         1     44161    44161.0\n",
       "5  @terranoticiasbr         1     44160    44160.0\n",
       "6          @canaln_        31     31715     1023.0\n",
       "7  @jacquelinabravo         1     31299    31299.0\n",
       "8       @alokadalis         2     27305    13652.0\n",
       "9   @colrichardkemp         1     23611    23611.0"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_freq_mention = adv.word_frequency(extracted_tweets['mentions'].str.join(' '), \n",
    "                                       extracted_tweets['followers'].fillna(0))\n",
    "                                    #.sort_values(['abs_freq'], ascending=False).head(20)\n",
    "word_freq_mention.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <td>7</td>\n",
       "      <td>304059</td>\n",
       "      <td>43437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>📣</td>\n",
       "      <td>3</td>\n",
       "      <td>295880</td>\n",
       "      <td>98627.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>🚔</td>\n",
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       "      <td>295632</td>\n",
       "      <td>147816.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>👮🏿‍♀️</td>\n",
       "      <td>2</td>\n",
       "      <td>295632</td>\n",
       "      <td>147816.0</td>\n",
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       "      <th>6</th>\n",
       "      <td>👮🏿‍♂️</td>\n",
       "      <td>2</td>\n",
       "      <td>295632</td>\n",
       "      <td>147816.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>👉</td>\n",
       "      <td>7</td>\n",
       "      <td>221454</td>\n",
       "      <td>31636.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>😝</td>\n",
       "      <td>2</td>\n",
       "      <td>214546</td>\n",
       "      <td>107273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>🇪🇨</td>\n",
       "      <td>4</td>\n",
       "      <td>153264</td>\n",
       "      <td>38316.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    word  abs_freq  wtd_freq  rel_value\n",
       "0      😭        39    434614    11144.0\n",
       "1      🖤         1    377740   377740.0\n",
       "2      🤔         7    304059    43437.0\n",
       "3      📣         3    295880    98627.0\n",
       "4      🚔         2    295632   147816.0\n",
       "5  👮🏿‍♀️         2    295632   147816.0\n",
       "6  👮🏿‍♂️         2    295632   147816.0\n",
       "7      👉         7    221454    31636.0\n",
       "8      😝         2    214546   107273.0\n",
       "9     🇪🇨         4    153264    38316.0"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_freq_emoji = adv.word_frequency(extracted_tweets['emoji'].str.join(' '), \n",
    "                                       extracted_tweets['followers'].fillna(0))#.sort_values(['abs_freq'], ascending=False).head(20)\n",
    "word_freq_emoji.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[':loudly_crying_face:',\n",
       " ':black_heart:',\n",
       " ':thinking_face:',\n",
       " ':megaphone:',\n",
       " ':oncoming_police_car:',\n",
       " ':woman_police_officer_dark_skin_tone:',\n",
       " ':man_police_officer_dark_skin_tone:',\n",
       " ':backhand_index_pointing_right:',\n",
       " ':squinting_face_with_tongue:',\n",
       " ':Ecuador:']"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[adv.emoji_dict.emoji_dict[k] for k in word_freq_emoji['word'][:10]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <td>7</td>\n",
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       "      <td>:thinking_face:</td>\n",
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       "      <td>🚔</td>\n",
       "      <td>2</td>\n",
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       "      <td>147816.0</td>\n",
       "      <td>:oncoming_police_car:</td>\n",
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       "      <td>👮🏿‍♀️</td>\n",
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       "      <td>295632</td>\n",
       "      <td>147816.0</td>\n",
       "      <td>:woman_police_officer_dark_skin_tone:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>👮🏿‍♂️</td>\n",
       "      <td>2</td>\n",
       "      <td>295632</td>\n",
       "      <td>147816.0</td>\n",
       "      <td>:man_police_officer_dark_skin_tone:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>👉</td>\n",
       "      <td>7</td>\n",
       "      <td>221454</td>\n",
       "      <td>31636.0</td>\n",
       "      <td>:backhand_index_pointing_right:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>😝</td>\n",
       "      <td>2</td>\n",
       "      <td>214546</td>\n",
       "      <td>107273.0</td>\n",
       "      <td>:squinting_face_with_tongue:</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>🇪🇨</td>\n",
       "      <td>4</td>\n",
       "      <td>153264</td>\n",
       "      <td>38316.0</td>\n",
       "      <td>:Ecuador:</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    word  abs_freq  wtd_freq  rel_value                             emoji_text\n",
       "0      😭        39    434614    11144.0                   :loudly_crying_face:\n",
       "1      🖤         1    377740   377740.0                          :black_heart:\n",
       "2      🤔         7    304059    43437.0                        :thinking_face:\n",
       "3      📣         3    295880    98627.0                            :megaphone:\n",
       "4      🚔         2    295632   147816.0                  :oncoming_police_car:\n",
       "5  👮🏿‍♀️         2    295632   147816.0  :woman_police_officer_dark_skin_tone:\n",
       "6  👮🏿‍♂️         2    295632   147816.0    :man_police_officer_dark_skin_tone:\n",
       "7      👉         7    221454    31636.0        :backhand_index_pointing_right:\n",
       "8      😝         2    214546   107273.0           :squinting_face_with_tongue:\n",
       "9     🇪🇨         4    153264    38316.0                              :Ecuador:"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_freq_emoji[:10].assign(emoji_text=[adv.emoji_dict.emoji_dict[k] for k in word_freq_emoji['word'][:10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "sotu_retweets = np.load('tweets_dict.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
       "      <td>GusRodrigues4</td>\n",
       "      <td>752</td>\n",
       "      <td>None</td>\n",
       "      <td>none</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sat Oct 20 01:57:17 +0000 2018</td>\n",
       "      <td>[0, 140]</td>\n",
       "      <td>[{'text': 'SemanaDeMayordomia', 'indices': [16, 35]}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'url': 'https://t.co/gMo19IEnh6', 'expanded_url': 'https://twitter.com/i/web/status/1053465144875991045', 'display...</td>\n",
       "      <td>...</td>\n",
       "      <td>333333</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>IASDEsperanza</td>\n",
       "      <td>698</td>\n",
       "      <td>None</td>\n",
       "      <td>none</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sat Oct 20 01:57:18 +0000 2018</td>\n",
       "      <td>[17, 23]</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>333333</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>mcthuglife666</td>\n",
       "      <td>1168</td>\n",
       "      <td>None</td>\n",
       "      <td>none</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 125 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  contributors  coordinates coordinates-coordinates coordinates-type  \\\n",
       "0         None          NaN                     NaN              NaN   \n",
       "1         None          NaN                     NaN              NaN   \n",
       "2         None          NaN                     NaN              NaN   \n",
       "3         None          NaN                     NaN              NaN   \n",
       "4         None          NaN                     NaN              NaN   \n",
       "\n",
       "                       created_at display_text_range  \\\n",
       "0  Sat Oct 20 01:57:03 +0000 2018           [30, 53]   \n",
       "1  Sat Oct 20 01:57:10 +0000 2018           [28, 44]   \n",
       "2  Sat Oct 20 01:57:12 +0000 2018           [13, 23]   \n",
       "3  Sat Oct 20 01:57:17 +0000 2018           [0, 140]   \n",
       "4  Sat Oct 20 01:57:18 +0000 2018           [17, 23]   \n",
       "\n",
       "                                       entities-hashtags entities-media  \\\n",
       "0                                                     []            NaN   \n",
       "1                                                     []            NaN   \n",
       "2                                                     []            NaN   \n",
       "3  [{'text': 'SemanaDeMayordomia', 'indices': [16, 35]}]            NaN   \n",
       "4                                                     []            NaN   \n",
       "\n",
       "  entities-symbols  \\\n",
       "0               []   \n",
       "1               []   \n",
       "2               []   \n",
       "3               []   \n",
       "4               []   \n",
       "\n",
       "                                                                                                             entities-urls  \\\n",
       "0                                                                                                                       []   \n",
       "1                                                                                                                       []   \n",
       "2                                                                                                                       []   \n",
       "3  [{'url': 'https://t.co/gMo19IEnh6', 'expanded_url': 'https://twitter.com/i/web/status/1053465144875991045', 'display...   \n",
       "4                                                                                                                       []   \n",
       "\n",
       "       ...       user-profile_text_color user-profile_use_background_image  \\\n",
       "0      ...                        333333                              True   \n",
       "1      ...                        333333                              True   \n",
       "2      ...                        333333                              True   \n",
       "3      ...                        333333                              True   \n",
       "4      ...                        333333                              True   \n",
       "\n",
       "  user-protected user-screen_name user-statuses_count user-time_zone  \\\n",
       "0          False          geriitx                 293           None   \n",
       "1          False     MurgaRodolfo                 256           None   \n",
       "2          False    GusRodrigues4                 752           None   \n",
       "3          False    IASDEsperanza                 698           None   \n",
       "4          False    mcthuglife666                1168           None   \n",
       "\n",
       "   user-translator_type                                             user-url  \\\n",
       "0                  none  https://m.facebook.com/geriita.ab?__user=1571199766   \n",
       "1                  none                                                 None   \n",
       "2                  none                                                 None   \n",
       "3                  none                                                 None   \n",
       "4                  none                                                 None   \n",
       "\n",
       "  user-utc_offset  user-verified  \n",
       "0            None          False  \n",
       "1            None          False  \n",
       "2            None          False  \n",
       "3            None          False  \n",
       "4            None          False  \n",
       "\n",
       "[5 rows x 125 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def buildDataFrameFromDict(mapping):\n",
    "    df=[]\n",
    "    for f in mapping:\n",
    "        f_n = {}\n",
    "        for k,item in f.items():\n",
    "            if isinstance(item,dict):\n",
    "                for i,j in item.items():\n",
    "                    f_n[k+'-'+i] = j\n",
    "            else:\n",
    "                f_n[k] = f[k]\n",
    "        df.append(f_n)\n",
    "    \n",
    "    df = pd.DataFrame(df)\n",
    "    return df\n",
    "\n",
    "sotu = buildDataFrameFromDict(sotu_retweets)\n",
    "sotu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "contributors\n",
      "coordinates\n",
      "coordinates-coordinates\n",
      "coordinates-type\n",
      "created_at\n",
      "display_text_range\n",
      "entities-hashtags\n",
      "entities-media\n",
      "entities-symbols\n",
      "entities-urls\n",
      "entities-user_mentions\n",
      "extended_entities-media\n",
      "extended_tweet-display_text_range\n",
      "extended_tweet-entities\n",
      "extended_tweet-extended_entities\n",
      "extended_tweet-full_text\n",
      "favorite_count\n",
      "favorited\n",
      "filter_level\n",
      "geo\n",
      "geo-coordinates\n",
      "geo-type\n",
      "id\n",
      "id_str\n",
      "in_reply_to_screen_name\n",
      "in_reply_to_status_id\n",
      "in_reply_to_status_id_str\n",
      "in_reply_to_user_id\n",
      "in_reply_to_user_id_str\n",
      "is_quote_status\n",
      "lang\n",
      "place-attributes\n",
      "place-bounding_box\n",
      "place-country\n",
      "place-country_code\n",
      "place-full_name\n",
      "place-id\n",
      "place-name\n",
      "place-place_type\n",
      "place-url\n",
      "possibly_sensitive\n",
      "quote_count\n",
      "quoted_status-contributors\n",
      "quoted_status-coordinates\n",
      "quoted_status-created_at\n",
      "quoted_status-display_text_range\n",
      "quoted_status-entities\n",
      "quoted_status-extended_entities\n",
      "quoted_status-extended_tweet\n",
      "quoted_status-favorite_count\n",
      "quoted_status-favorited\n",
      "quoted_status-filter_level\n",
      "quoted_status-geo\n",
      "quoted_status-id\n",
      "quoted_status-id_str\n",
      "quoted_status-in_reply_to_screen_name\n",
      "quoted_status-in_reply_to_status_id\n",
      "quoted_status-in_reply_to_status_id_str\n",
      "quoted_status-in_reply_to_user_id\n",
      "quoted_status-in_reply_to_user_id_str\n",
      "quoted_status-is_quote_status\n",
      "quoted_status-lang\n",
      "quoted_status-place\n",
      "quoted_status-possibly_sensitive\n",
      "quoted_status-quote_count\n",
      "quoted_status-quoted_status_id\n",
      "quoted_status-quoted_status_id_str\n",
      "quoted_status-reply_count\n",
      "quoted_status-retweet_count\n",
      "quoted_status-retweeted\n",
      "quoted_status-source\n",
      "quoted_status-text\n",
      "quoted_status-truncated\n",
      "quoted_status-user\n",
      "quoted_status_id\n",
      "quoted_status_id_str\n",
      "quoted_status_permalink-display\n",
      "quoted_status_permalink-expanded\n",
      "quoted_status_permalink-url\n",
      "reply_count\n",
      "retweet_count\n",
      "retweeted\n",
      "source\n",
      "text\n",
      "timestamp_ms\n",
      "truncated\n",
      "user-contributors_enabled\n",
      "user-created_at\n",
      "user-default_profile\n",
      "user-default_profile_image\n",
      "user-description\n",
      "user-favourites_count\n",
      "user-follow_request_sent\n",
      "user-followers_count\n",
      "user-following\n",
      "user-friends_count\n",
      "user-geo_enabled\n",
      "user-id\n",
      "user-id_str\n",
      "user-is_translator\n",
      "user-lang\n",
      "user-listed_count\n",
      "user-location\n",
      "user-name\n",
      "user-notifications\n",
      "user-profile_background_color\n",
      "user-profile_background_image_url\n",
      "user-profile_background_image_url_https\n",
      "user-profile_background_tile\n",
      "user-profile_banner_url\n",
      "user-profile_image_url\n",
      "user-profile_image_url_https\n",
      "user-profile_link_color\n",
      "user-profile_sidebar_border_color\n",
      "user-profile_sidebar_fill_color\n",
      "user-profile_text_color\n",
      "user-profile_use_background_image\n",
      "user-protected\n",
      "user-screen_name\n",
      "user-statuses_count\n",
      "user-time_zone\n",
      "user-translator_type\n",
      "user-url\n",
      "user-utc_offset\n",
      "user-verified\n"
     ]
    }
   ],
   "source": [
    "for c in sotu.columns:\n",
    "    print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = sotu.sample(10)['user-screen_name']\n",
    "sotu['retweeted_status-user-screen_name'] = np.random.choice(data,len(sotu))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>user-screen_name</th>\n",
       "      <th>retweeted_status-user-screen_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>geriitx</td>\n",
       "      <td>hhbacigalupo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MurgaRodolfo</td>\n",
       "      <td>hhbacigalupo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GusRodrigues4</td>\n",
       "      <td>mpauta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IASDEsperanza</td>\n",
       "      <td>hhbacigalupo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mcthuglife666</td>\n",
       "      <td>casidi13243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  user-screen_name retweeted_status-user-screen_name\n",
       "0          geriitx                      hhbacigalupo\n",
       "1     MurgaRodolfo                      hhbacigalupo\n",
       "2    GusRodrigues4                            mpauta\n",
       "3    IASDEsperanza                      hhbacigalupo\n",
       "4    mcthuglife666                       casidi13243"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sotu[['user-screen_name','retweeted_status-user-screen_name']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nodes in RT network: 505\n",
      "Edges in RT network: 855\n"
     ]
    }
   ],
   "source": [
    "G_rt = nx.from_pandas_edgelist(\n",
    "    sotu,\n",
    "    source = 'user-screen_name', \n",
    "    target = 'retweeted_status-user-screen_name',\n",
    "    create_using = nx.DiGraph())\n",
    "    \n",
    "print('Nodes in RT network:', len(G_rt.nodes()))\n",
    "print('Edges in RT network:', len(G_rt.edges()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nodes in reply network: 796\n",
      "Edges in reply network: 708\n"
     ]
    }
   ],
   "source": [
    "G_reply = nx.from_pandas_edgelist(\n",
    "    sotu,\n",
    "    source = 'user-screen_name', \n",
    "    target = 'in_reply_to_screen_name',\n",
    "    create_using = nx.DiGraph())\n",
    "    \n",
    "print('Nodes in reply network:', len(G_reply.nodes()))\n",
    "\n",
    "print('Edges in reply network:', len(G_reply.edges()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.random_layout(G_rt)\n",
    "\n",
    "sizes = [x[1] for x in G_rt.degree()]\n",
    "\n",
    "nx.draw_networkx(G_rt, pos, \n",
    "    with_labels = False, \n",
    "    node_size = sizes,\n",
    "    width = 0.1, alpha = 0.7,\n",
    "    arrowsize = 2, linewidths = 0)\n",
    "\n",
    "plt.savefig('snapshot/lima_tweets_influencing_graph.png')\n",
    "plt.axis('off'); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.random_layout(G_reply)\n",
    "sizes = [x[1] for x in G_reply.degree()]\n",
    "\n",
    "nx.draw_networkx(G_reply, pos, \n",
    "    with_labels = False, \n",
    "    node_size = sizes,\n",
    "    width = 0.1, alpha = 0.7,\n",
    "    arrowsize = 2, linewidths = 0)\n",
    "\n",
    "plt.axis('off'); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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>screen_name</th>\n",
       "      <th>degree_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hhbacigalupo</td>\n",
       "      <td>0.208333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NOTIELMOMENTO</td>\n",
       "      <td>0.178571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>casidi13243</td>\n",
       "      <td>0.176587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mpauta</td>\n",
       "      <td>0.174603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Rockmanita</td>\n",
       "      <td>0.168651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      screen_name  degree_centrality\n",
       "1    hhbacigalupo           0.208333\n",
       "12  NOTIELMOMENTO           0.178571\n",
       "7     casidi13243           0.176587\n",
       "4          mpauta           0.174603\n",
       "17     Rockmanita           0.168651"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>screen_name</th>\n",
       "      <th>degree_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>None</td>\n",
       "      <td>0.441509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>canalN_</td>\n",
       "      <td>0.015094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>SolCn</td>\n",
       "      <td>0.005031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>CesarNakazaki</td>\n",
       "      <td>0.005031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>IDL_R</td>\n",
       "      <td>0.003774</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       screen_name  degree_centrality\n",
       "7             None           0.441509\n",
       "105        canalN_           0.015094\n",
       "83           SolCn           0.005031\n",
       "400  CesarNakazaki           0.005031\n",
       "448          IDL_R           0.003774"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "column_names = ['screen_name', 'degree_centrality']\n",
    "rt_centrality = nx.in_degree_centrality(G_rt)\n",
    "\n",
    "reply_centrality = nx.in_degree_centrality(G_reply)\n",
    "\n",
    "rt = pd.DataFrame(list(rt_centrality.items()), columns = column_names)\n",
    "reply = pd.DataFrame(list(reply_centrality.items()), columns = column_names)\n",
    "\n",
    "display(rt.sort_values('degree_centrality', ascending = False).head())\n",
    "\n",
    "display(reply.sort_values('degree_centrality', ascending = False).head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>screen_name</th>\n",
       "      <th>betweenness_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NOTIELMOMENTO</td>\n",
       "      <td>0.004589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>FedericoClemen8</td>\n",
       "      <td>0.004563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>marioporlavida</td>\n",
       "      <td>0.003482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Rockmanita</td>\n",
       "      <td>0.003354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hhbacigalupo</td>\n",
       "      <td>0.003119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        screen_name  betweenness_centrality\n",
       "12    NOTIELMOMENTO                0.004589\n",
       "27  FedericoClemen8                0.004563\n",
       "10   marioporlavida                0.003482\n",
       "17       Rockmanita                0.003354\n",
       "1      hhbacigalupo                0.003119"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>screen_name</th>\n",
       "      <th>betweenness_centrality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>mindreaux</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>famasem</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Ldavidesc</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>geriitx</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>rolo18al</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    screen_name  betweenness_centrality\n",
       "413   mindreaux                0.000003\n",
       "442     famasem                0.000003\n",
       "405   Ldavidesc                0.000002\n",
       "0       geriitx                0.000000\n",
       "526    rolo18al                0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "column_names = ['screen_name', 'betweenness_centrality']\n",
    "# Generate betweenness centrality for retweets \n",
    "rt_centrality = nx.betweenness_centrality(G_rt)\n",
    "\n",
    "# Generate betweenness centrality for replies \n",
    "reply_centrality = nx.betweenness_centrality(G_reply)\n",
    "\n",
    "# Store centralities in data frames\n",
    "rt = pd.DataFrame(list(rt_centrality.items()), columns = column_names)\n",
    "reply = pd.DataFrame(list(reply_centrality.items()), columns = column_names)\n",
    "\n",
    "# Print first five results in descending order of centrality\n",
    "display(rt.sort_values('betweenness_centrality', ascending = False).head())\n",
    "\n",
    "# Print first five results in descending order of centrality\n",
    "display(reply.sort_values('betweenness_centrality', ascending = False).head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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>screen_name</th>\n",
       "      <th>degree_rt</th>\n",
       "      <th>degree_reply</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hhbacigalupo</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mpauta</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>casidi13243</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>marioporlavida</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NOTIELMOMENTO</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       screen_name  degree_rt  degree_reply  ratio\n",
       "1     hhbacigalupo        105             0    0.0\n",
       "4           mpauta         88             0    0.0\n",
       "7      casidi13243         89             0    0.0\n",
       "10  marioporlavida         82             0    0.0\n",
       "12   NOTIELMOMENTO         90             0    0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "column_names = ['screen_name', 'degree']\n",
    "\n",
    "degree_rt = pd.DataFrame(list(G_rt.in_degree()), columns = column_names)\n",
    "degree_reply = pd.DataFrame(list(G_reply.in_degree()), columns = column_names)\n",
    "\n",
    "ratio = degree_rt.merge(degree_reply, on = 'screen_name', suffixes = ('_rt', '_reply'))\n",
    "\n",
    "ratio['ratio'] = ratio['degree_reply'] / ratio['degree_rt']\n",
    "\n",
    "ratio = ratio[ratio['degree_rt'] >= 5]\n",
    "\n",
    "display(ratio.sort_values('ratio', ascending = False).head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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