
                        # Libraries to help with reading and manipulating data
import numpy as np
import pandas as pd

# libraries for visualizations
import seaborn as sns
import matplotlib.pyplot as plt

# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)

import re
import string

from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer

# Library to suppress warnings or deprecation notes
import warnings

warnings.filterwarnings("ignore")


                        # You'll need to install NLTK if you don't have it already
!pip install nltk

Requirement already satisfied: nltk in /home/chris/.local/miniconda3/envs/cs109a/lib/python3.9/site-packages (3.6.5)
Requirement already satisfied: joblib in /home/chris/.local/miniconda3/envs/cs109a/lib/python3.9/site-packages (from nltk) (1.0.1)
Requirement already satisfied: tqdm in /home/chris/.local/miniconda3/envs/cs109a/lib/python3.9/site-packages (from nltk) (4.62.3)
Requirement already satisfied: click in /home/chris/.local/miniconda3/envs/cs109a/lib/python3.9/site-packages (from nltk) (8.0.1)
Requirement already satisfied: regex>=2021.8.3 in /home/chris/.local/miniconda3/envs/cs109a/lib/python3.9/site-packages (from nltk) (2021.11.10)


                        # Let's use the NLTK library
import nltk
from nltk.corpus import twitter_samples

# Download nltk data
nltk.download("twitter_samples")

[nltk_data] Downloading package twitter_samples to
[nltk_data]     /home/chris/nltk_data...
[nltk_data]   Package twitter_samples is already up-to-date!

True


                        twitter_samples

<TwitterCorpusReader in '/home/chris/nltk_data/corpora/twitter_samples'>


                        twitter_samples

<TwitterCorpusReader in '/home/chris/nltk_data/corpora/twitter_samples'>


                        twitter_samples.fileids()

['negative_tweets.json', 'positive_tweets.json', 'tweets.20150430-223406.json']


                        # Checking the sixth element:
print("Positive : ", twitter_samples.strings("positive_tweets.json")[5], "\n")
print("Negative : ", twitter_samples.strings("negative_tweets.json")[5])

Positive :  @BhaktisBanter @PallaviRuhail This one is irresistible :)
#FlipkartFashionFriday http://t.co/EbZ0L2VENM

Negative :  oh god, my babies' faces :( https://t.co/9fcwGvaki0


                        positive_tw = twitter_samples.strings("positive_tweets.json")
negative_tw = twitter_samples.strings("negative_tweets.json")


                        print(type(positive_tw))
print(type(negative_tw))

<class 'list'>
<class 'list'>


                        positive_tw[5]

'@BhaktisBanter @PallaviRuhail This one is irresistible :)\n#FlipkartFashionFriday http://t.co/EbZ0L2VENM'


                        negative_tw[5]

"oh god, my babies' faces :( https://t.co/9fcwGvaki0"


                        print("Positive tweets: ", len(positive_tw))
print("Negative tweets: ", len(negative_tw))

Positive tweets:  5000
Negative tweets:  5000


                        positive_tweets = pd.DataFrame(positive_tw, columns=["tweets"])
positive_tweets


                        negative_tweets = pd.DataFrame(negative_tw, columns=["tweets"])
negative_tweets


                        positive_tweets["sentiment"] = 1
positive_tweets["sent_descr"] = "positive"

negative_tweets["sentiment"] = 0
negative_tweets["sent_descr"] = "negative"

positive_tweets.head()


                        positive_tweets.head()


                        negative_tweets.head()


                        # Creating the copy of the Positive tweets and adding it to a new DataFrame
tweets_df = positive_tweets.copy()
tweets_df


                        # Adding the negative tweets to our new DataFrame "tweets".
tweets_df = tweets_df.append(negative_tweets, ignore_index=True)
tweets_df


                        # Resetting the index
tweets_df.reset_index(drop=True, inplace=True)
tweets_df


                        # Let's visualize and verify that our data is consistent.

plt.figure(figsize=(7, 3.5))
sns.set_theme(style="white", palette="nipy_spectral")
sns.countplot(data=tweets_df, x="sent_descr", hue="sent_descr")

<AxesSubplot:xlabel='sent_descr', ylabel='count'>


                        tweets_df = tweets_df.drop(["sent_descr"], axis=1)


                        # Before we start, let's create a copy of our data so we can compare all the changes later.
initial_df = tweets_df.copy()


                        def lowercase_func(tweets):
    return tweets.lower()


tweets_df["tweets"] = tweets_df["tweets"].apply(lambda x: lowercase_func(x))
tweets_df


                        def find_url_in_tweets(tweets):
    return tweets.find("http")


tweets_df["find_url"] = tweets_df["tweets"].apply(lambda m: find_url_in_tweets(m))
tweets_df


                        with_url = range(140)


match_url = tweets_df[tweets_df["find_url"].isin(with_url)]
match_url.head(10)


                        # Looking at the datapoint with index 0 to confirm that it has an URL.
match_url["tweets"].iloc[0]

'@bhaktisbanter @pallaviruhail this one is irresistible :)\n#flipkartfashionfriday http://t.co/ebz0l2venm'


                        def remove_urls(tweets):
    url_pattern = re.compile(r"http[s]?://\S+|www\.\S+")
    return url_pattern.sub(r"", tweets)


tweets_df["tweets"] = tweets_df["tweets"].apply(remove_urls)
tweets_df.head(10)


                        tweets_df["tweets"].iloc[5]

'@bhaktisbanter @pallaviruhail this one is irresistible :)\n#flipkartfashionfriday '


                        # Since we won't need anymore the column find_url, we will drop it.
tweets_df = tweets_df.drop(["find_url"], axis=1)


                        # We will use the library emot, which is open source.
# we'll need version 2.1 specifically
!pip install emot==2.1

Requirement already satisfied: emot==2.1 in /home/chris/.local/miniconda3/envs/cs109a/lib/python3.9/site-packages (2.1)


                        from emot.emo_unicode import UNICODE_EMO,EMOTICONS


                        UNICODE_EMO['😂'].replace(",", " ").replace(":", " ").split()
#UNICODE_EMO['😂']

['face_with_tears_of_joy']


                        EMOTICONS

{':‑\\)': 'Happy face or smiley',
 ':\\)': 'Happy face or smiley',
 ':-\\]': 'Happy face or smiley',
 ':\\]': 'Happy face or smiley',
 ':-3': 'Happy face smiley',
 ':3': 'Happy face smiley',
 ':->': 'Happy face smiley',
 ':>': 'Happy face smiley',
 '8-\\)': 'Happy face smiley',
 ':o\\)': 'Happy face smiley',
 ':-\\}': 'Happy face smiley',
 ':\\}': 'Happy face smiley',
 ':-\\)': 'Happy face smiley',
 ':c\\)': 'Happy face smiley',
 ':\\^\\)': 'Happy face smiley',
 '=\\]': 'Happy face smiley',
 '=\\)': 'Happy face smiley',
 ':‑D': 'Laughing, big grin or laugh with glasses',
 ':D': 'Laughing, big grin or laugh with glasses',
 '8‑D': 'Laughing, big grin or laugh with glasses',
 '8D': 'Laughing, big grin or laugh with glasses',
 'X‑D': 'Laughing, big grin or laugh with glasses',
 'XD': 'Laughing, big grin or laugh with glasses',
 '=D': 'Laughing, big grin or laugh with glasses',
 '=3': 'Laughing, big grin or laugh with glasses',
 'B\\^D': 'Laughing, big grin or laugh with glasses',
 ':-\\)\\)': 'Very happy',
 ':‑\\(': 'Frown, sad, andry or pouting',
 ':-\\(': 'Frown, sad, andry or pouting',
 ':\\(': 'Frown, sad, andry or pouting',
 ':‑c': 'Frown, sad, andry or pouting',
 ':c': 'Frown, sad, andry or pouting',
 ':‑<': 'Frown, sad, andry or pouting',
 ':<': 'Frown, sad, andry or pouting',
 ':‑\\[': 'Frown, sad, andry or pouting',
 ':\\[': 'Frown, sad, andry or pouting',
 ':-\\|\\|': 'Frown, sad, andry or pouting',
 '>:\\[': 'Frown, sad, andry or pouting',
 ':\\{': 'Frown, sad, andry or pouting',
 ':@': 'Frown, sad, andry or pouting',
 '>:\\(': 'Frown, sad, andry or pouting',
 ":'‑\\(": 'Crying',
 ":'\\(": 'Crying',
 ":'‑\\)": 'Tears of happiness',
 ":'\\)": 'Tears of happiness',
 "D‑':": 'Horror',
 'D:<': 'Disgust',
 'D:': 'Sadness',
 'D8': 'Great dismay',
 'D;': 'Great dismay',
 'D=': 'Great dismay',
 'DX': 'Great dismay',
 ':‑O': 'Surprise',
 ':O': 'Surprise',
 ':‑o': 'Surprise',
 ':o': 'Surprise',
 ':-0': 'Shock',
 '8‑0': 'Yawn',
 '>:O': 'Yawn',
 ':-\\*': 'Kiss',
 ':\\*': 'Kiss',
 ':X': 'Kiss',
 ';‑\\)': 'Wink or smirk',
 ';\\)': 'Wink or smirk',
 '\\*-\\)': 'Wink or smirk',
 '\\*\\)': 'Wink or smirk',
 ';‑\\]': 'Wink or smirk',
 ';\\]': 'Wink or smirk',
 ';\\^\\)': 'Wink or smirk',
 ':‑,': 'Wink or smirk',
 ';D': 'Wink or smirk',
 ':‑P': 'Tongue sticking out, cheeky, playful or blowing a raspberry',
 ':P': 'Tongue sticking out, cheeky, playful or blowing a raspberry',
 'X‑P': 'Tongue sticking out, cheeky, playful or blowing a raspberry',
 'XP': 'Tongue sticking out, cheeky, playful or blowing a raspberry',
 ':‑Þ': 'Tongue sticking out, cheeky, playful or blowing a raspberry',
 ':Þ': 'Tongue sticking out, cheeky, playful or blowing a raspberry',
 ':b': 'Tongue sticking out, cheeky, playful or blowing a raspberry',
 'd:': 'Tongue sticking out, cheeky, playful or blowing a raspberry',
 '=p': 'Tongue sticking out, cheeky, playful or blowing a raspberry',
 '>:P': 'Tongue sticking out, cheeky, playful or blowing a raspberry',
 ':‑/': 'Skeptical, annoyed, undecided, uneasy or hesitant',
 ':/': 'Skeptical, annoyed, undecided, uneasy or hesitant',
 ':-[.]': 'Skeptical, annoyed, undecided, uneasy or hesitant',
 '>:[(\\\\)]': 'Skeptical, annoyed, undecided, uneasy or hesitant',
 '>:/': 'Skeptical, annoyed, undecided, uneasy or hesitant',
 ':[(\\\\)]': 'Skeptical, annoyed, undecided, uneasy or hesitant',
 '=/': 'Skeptical, annoyed, undecided, uneasy or hesitant',
 '=[(\\\\)]': 'Skeptical, annoyed, undecided, uneasy or hesitant',
 ':L': 'Skeptical, annoyed, undecided, uneasy or hesitant',
 '=L': 'Skeptical, annoyed, undecided, uneasy or hesitant',
 ':S': 'Skeptical, annoyed, undecided, uneasy or hesitant',
 ':‑\\|': 'Straight face',
 ':\\|': 'Straight face',
 ':$': 'Embarrassed or blushing',
 ':‑x': 'Sealed lips or wearing braces or tongue-tied',
 ':x': 'Sealed lips or wearing braces or tongue-tied',
 ':‑#': 'Sealed lips or wearing braces or tongue-tied',
 ':#': 'Sealed lips or wearing braces or tongue-tied',
 ':‑&': 'Sealed lips or wearing braces or tongue-tied',
 ':&': 'Sealed lips or wearing braces or tongue-tied',
 'O:‑\\)': 'Angel, saint or innocent',
 'O:\\)': 'Angel, saint or innocent',
 '0:‑3': 'Angel, saint or innocent',
 '0:3': 'Angel, saint or innocent',
 '0:‑\\)': 'Angel, saint or innocent',
 '0:\\)': 'Angel, saint or innocent',
 ':‑b': 'Tongue sticking out, cheeky, playful or blowing a raspberry',
 '0;\\^\\)': 'Angel, saint or innocent',
 '>:‑\\)': 'Evil or devilish',
 '>:\\)': 'Evil or devilish',
 '\\}:‑\\)': 'Evil or devilish',
 '\\}:\\)': 'Evil or devilish',
 '3:‑\\)': 'Evil or devilish',
 '3:\\)': 'Evil or devilish',
 '>;\\)': 'Evil or devilish',
 '\\|;‑\\)': 'Cool',
 '\\|‑O': 'Bored',
 ':‑J': 'Tongue-in-cheek',
 '#‑\\)': 'Party all night',
 '%‑\\)': 'Drunk or confused',
 '%\\)': 'Drunk or confused',
 ':-###..': 'Being sick',
 ':###..': 'Being sick',
 '<:‑\\|': 'Dump',
 '\\(>_<\\)': 'Troubled',
 '\\(>_<\\)>': 'Troubled',
 "\\(';'\\)": 'Baby',
 '\\(\\^\\^>``': 'Nervous or Embarrassed or Troubled or Shy or Sweat drop',
 '\\(\\^_\\^;\\)': 'Nervous or Embarrassed or Troubled or Shy or Sweat drop',
 '\\(-_-;\\)': 'Nervous or Embarrassed or Troubled or Shy or Sweat drop',
 '\\(~_~;\\) \\(・\\.・;\\)': 'Nervous or Embarrassed or Troubled or Shy or Sweat drop',
 '\\(-_-\\)zzz': 'Sleeping',
 '\\(\\^_-\\)': 'Wink',
 '\\(\\(\\+_\\+\\)\\)': 'Confused',
 '\\(\\+o\\+\\)': 'Confused',
 '\\(o\\|o\\)': 'Ultraman',
 '\\^_\\^': 'Joyful',
 '\\(\\^_\\^\\)/': 'Joyful',
 '\\(\\^O\\^\\)／': 'Joyful',
 '\\(\\^o\\^\\)／': 'Joyful',
 '\\(__\\)': 'Kowtow as a sign of respect, or dogeza for apology',
 '_\\(\\._\\.\\)_': 'Kowtow as a sign of respect, or dogeza for apology',
 '<\\(_ _\\)>': 'Kowtow as a sign of respect, or dogeza for apology',
 '<m\\(__\\)m>': 'Kowtow as a sign of respect, or dogeza for apology',
 'm\\(__\\)m': 'Kowtow as a sign of respect, or dogeza for apology',
 'm\\(_ _\\)m': 'Kowtow as a sign of respect, or dogeza for apology',
 "\\('_'\\)": 'Sad or Crying',
 '\\(/_;\\)': 'Sad or Crying',
 '\\(T_T\\) \\(;_;\\)': 'Sad or Crying',
 '\\(;_;': 'Sad of Crying',
 '\\(;_:\\)': 'Sad or Crying',
 '\\(;O;\\)': 'Sad or Crying',
 '\\(:_;\\)': 'Sad or Crying',
 '\\(ToT\\)': 'Sad or Crying',
 ';_;': 'Sad or Crying',
 ';-;': 'Sad or Crying',
 ';n;': 'Sad or Crying',
 ';;': 'Sad or Crying',
 'Q\\.Q': 'Sad or Crying',
 'T\\.T': 'Sad or Crying',
 'QQ': 'Sad or Crying',
 'Q_Q': 'Sad or Crying',
 '\\(-\\.-\\)': 'Shame',
 '\\(-_-\\)': 'Shame',
 '\\(一一\\)': 'Shame',
 '\\(；一_一\\)': 'Shame',
 '\\(=_=\\)': 'Tired',
 '\\(=\\^\\·\\^=\\)': 'cat',
 '\\(=\\^\\·\\·\\^=\\)': 'cat',
 '=_\\^=\t': 'cat',
 '\\(\\.\\.\\)': 'Looking down',
 '\\(\\._\\.\\)': 'Looking down',
 '\\^m\\^': 'Giggling with hand covering mouth',
 '\\(\\・\\・?': 'Confusion',
 '\\(?_?\\)': 'Confusion',
 '>\\^_\\^<': 'Normal Laugh',
 '<\\^!\\^>': 'Normal Laugh',
 '\\^/\\^': 'Normal Laugh',
 '\\（\\*\\^_\\^\\*）': 'Normal Laugh',
 '\\(\\^<\\^\\) \\(\\^\\.\\^\\)': 'Normal Laugh',
 '\\(^\\^\\)': 'Normal Laugh',
 '\\(\\^\\.\\^\\)': 'Normal Laugh',
 '\\(\\^_\\^\\.\\)': 'Normal Laugh',
 '\\(\\^_\\^\\)': 'Normal Laugh',
 '\\(\\^\\^\\)': 'Normal Laugh',
 '\\(\\^J\\^\\)': 'Normal Laugh',
 '\\(\\*\\^\\.\\^\\*\\)': 'Normal Laugh',
 '\\(\\^—\\^\\）': 'Normal Laugh',
 '\\(#\\^\\.\\^#\\)': 'Normal Laugh',
 '\\（\\^—\\^\\）': 'Waving',
 '\\(;_;\\)/~~~': 'Waving',
 '\\(\\^\\.\\^\\)/~~~': 'Waving',
 '\\(-_-\\)/~~~ \\($\\·\\·\\)/~~~': 'Waving',
 '\\(T_T\\)/~~~': 'Waving',
 '\\(ToT\\)/~~~': 'Waving',
 '\\(\\*\\^0\\^\\*\\)': 'Excited',
 '\\(\\*_\\*\\)': 'Amazed',
 '\\(\\*_\\*;': 'Amazed',
 '\\(\\+_\\+\\) \\(@_@\\)': 'Amazed',
 '\\(\\*\\^\\^\\)v': 'Laughing,Cheerful',
 '\\(\\^_\\^\\)v': 'Laughing,Cheerful',
 '\\(\\(d[-_-]b\\)\\)': 'Headphones,Listening to music',
 '\\(-"-\\)': 'Worried',
 '\\(ーー;\\)': 'Worried',
 '\\(\\^0_0\\^\\)': 'Eyeglasses',
 '\\(\\＾ｖ\\＾\\)': 'Happy',
 '\\(\\＾ｕ\\＾\\)': 'Happy',
 '\\(\\^\\)o\\(\\^\\)': 'Happy',
 '\\(\\^O\\^\\)': 'Happy',
 '\\(\\^o\\^\\)': 'Happy',
 '\\)\\^o\\^\\(': 'Happy',
 ':O o_O': 'Surprised',
 'o_0': 'Surprised',
 'o\\.O': 'Surpised',
 '\\(o\\.o\\)': 'Surprised',
 'oO': 'Surprised',
 '\\(\\*￣m￣\\)': 'Dissatisfied',
 '\\(‘A`\\)': 'Snubbed or Deflated'}


                        def convert_emojis(text):
    for emot in UNICODE_EMO:
        text = text.replace(
            emot,
            "  ".join(UNICODE_EMO[emot].replace(",", " ").replace(":", " ").split()),
        )
    return text


# Example:
emotions = "✨🎉😍👌 🤩 😂😎"
convert_emojis(emotions)

'sparklesparty_poppersmiling_face_with_heart-eyesOK_hand 🤩 face_with_tears_of_joysmiling_face_with_sunglasses'


                        def convert_emoticons(text):
    for emot in EMOTICONS:
        text = re.sub(
            u"(" + emot + ")", " ".join(EMOTICONS[emot].replace(",", "").split()), text
        )
    return text


                        # Example:
faces = ":) =) XD :D :( :D :3 ^_^"
convert_emoticons(faces)

'Happy face or smiley Happy face smiley Laughing big grin or laugh with glasses Laughing big grin or laugh with glasses Frown sad andry or pouting Laughing big grin or laugh with glasses Happy face smiley Joyful'


                        tweets_df["clean_tweets"] = tweets_df["tweets"].apply(convert_emoticons)
tweets_df["clean_tweets"] = tweets_df["clean_tweets"].apply(convert_emojis)
tweets_df.head(10)


                        def remove_mentions(tweets):
    mention_pattern = re.compile(r"(@[A-Za-z0-9]+)|[_]|([^0-9A-Za-z \t])|(\w+:\/\/\S+)")
    return mention_pattern.sub(r"", tweets)


tweets_df["clean_tweets"] = tweets_df["clean_tweets"].apply(remove_mentions)
tweets_df.head(5)


                        def remove_noise(tweet):
    tweet = re.sub("(@[A-Za-z0–9_]+)", "", tweet)
    tweet = "".join([char if char not in string.punctuation else " " for char in tweet])
    tweet = re.sub(" +", " ", tweet)
    tweet = re.sub("[0–9]+", "", tweet)
    tweet = re.sub("[^A-Za-z0–9_. ]+", "", tweet)

    return tweet


tweets_df["clean_tweets"] = tweets_df["clean_tweets"].apply(lambda x: remove_noise(x))

# Applying the lowercase method again given that emojis and emoticons were converted, and the dictionary that includes all
# of these emojis and emoticons have the definitions with the first letter in uppercase.

tweets_df["clean_tweets"] = tweets_df["clean_tweets"].apply(lambda x: lowercase_func(x))
tweets_df


                        from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
nltk.download('stopwords')

[nltk_data] Downloading package stopwords to /home/chris/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!

True


                        print("English:\n", stopwords.words("english"))
print("\nSpanish:\n", stopwords.words("spanish"))
print("\nRussian:\n", stopwords.words("russian"))

English:
 ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't"]

Spanish:
 ['de', 'la', 'que', 'el', 'en', 'y', 'a', 'los', 'del', 'se', 'las', 'por', 'un', 'para', 'con', 'no', 'una', 'su', 'al', 'lo', 'como', 'más', 'pero', 'sus', 'le', 'ya', 'o', 'este', 'sí', 'porque', 'esta', 'entre', 'cuando', 'muy', 'sin', 'sobre', 'también', 'me', 'hasta', 'hay', 'donde', 'quien', 'desde', 'todo', 'nos', 'durante', 'todos', 'uno', 'les', 'ni', 'contra', 'otros', 'ese', 'eso', 'ante', 'ellos', 'e', 'esto', 'mí', 'antes', 'algunos', 'qué', 'unos', 'yo', 'otro', 'otras', 'otra', 'él', 'tanto', 'esa', 'estos', 'mucho', 'quienes', 'nada', 'muchos', 'cual', 'poco', 'ella', 'estar', 'estas', 'algunas', 'algo', 'nosotros', 'mi', 'mis', 'tú', 'te', 'ti', 'tu', 'tus', 'ellas', 'nosotras', 'vosotros', 'vosotras', 'os', 'mío', 'mía', 'míos', 'mías', 'tuyo', 'tuya', 'tuyos', 'tuyas', 'suyo', 'suya', 'suyos', 'suyas', 'nuestro', 'nuestra', 'nuestros', 'nuestras', 'vuestro', 'vuestra', 'vuestros', 'vuestras', 'esos', 'esas', 'estoy', 'estás', 'está', 'estamos', 'estáis', 'están', 'esté', 'estés', 'estemos', 'estéis', 'estén', 'estaré', 'estarás', 'estará', 'estaremos', 'estaréis', 'estarán', 'estaría', 'estarías', 'estaríamos', 'estaríais', 'estarían', 'estaba', 'estabas', 'estábamos', 'estabais', 'estaban', 'estuve', 'estuviste', 'estuvo', 'estuvimos', 'estuvisteis', 'estuvieron', 'estuviera', 'estuvieras', 'estuviéramos', 'estuvierais', 'estuvieran', 'estuviese', 'estuvieses', 'estuviésemos', 'estuvieseis', 'estuviesen', 'estando', 'estado', 'estada', 'estados', 'estadas', 'estad', 'he', 'has', 'ha', 'hemos', 'habéis', 'han', 'haya', 'hayas', 'hayamos', 'hayáis', 'hayan', 'habré', 'habrás', 'habrá', 'habremos', 'habréis', 'habrán', 'habría', 'habrías', 'habríamos', 'habríais', 'habrían', 'había', 'habías', 'habíamos', 'habíais', 'habían', 'hube', 'hubiste', 'hubo', 'hubimos', 'hubisteis', 'hubieron', 'hubiera', 'hubieras', 'hubiéramos', 'hubierais', 'hubieran', 'hubiese', 'hubieses', 'hubiésemos', 'hubieseis', 'hubiesen', 'habiendo', 'habido', 'habida', 'habidos', 'habidas', 'soy', 'eres', 'es', 'somos', 'sois', 'son', 'sea', 'seas', 'seamos', 'seáis', 'sean', 'seré', 'serás', 'será', 'seremos', 'seréis', 'serán', 'sería', 'serías', 'seríamos', 'seríais', 'serían', 'era', 'eras', 'éramos', 'erais', 'eran', 'fui', 'fuiste', 'fue', 'fuimos', 'fuisteis', 'fueron', 'fuera', 'fueras', 'fuéramos', 'fuerais', 'fueran', 'fuese', 'fueses', 'fuésemos', 'fueseis', 'fuesen', 'sintiendo', 'sentido', 'sentida', 'sentidos', 'sentidas', 'siente', 'sentid', 'tengo', 'tienes', 'tiene', 'tenemos', 'tenéis', 'tienen', 'tenga', 'tengas', 'tengamos', 'tengáis', 'tengan', 'tendré', 'tendrás', 'tendrá', 'tendremos', 'tendréis', 'tendrán', 'tendría', 'tendrías', 'tendríamos', 'tendríais', 'tendrían', 'tenía', 'tenías', 'teníamos', 'teníais', 'tenían', 'tuve', 'tuviste', 'tuvo', 'tuvimos', 'tuvisteis', 'tuvieron', 'tuviera', 'tuvieras', 'tuviéramos', 'tuvierais', 'tuvieran', 'tuviese', 'tuvieses', 'tuviésemos', 'tuvieseis', 'tuviesen', 'teniendo', 'tenido', 'tenida', 'tenidos', 'tenidas', 'tened']

Russian:
 ['и', 'в', 'во', 'не', 'что', 'он', 'на', 'я', 'с', 'со', 'как', 'а', 'то', 'все', 'она', 'так', 'его', 'но', 'да', 'ты', 'к', 'у', 'же', 'вы', 'за', 'бы', 'по', 'только', 'ее', 'мне', 'было', 'вот', 'от', 'меня', 'еще', 'нет', 'о', 'из', 'ему', 'теперь', 'когда', 'даже', 'ну', 'вдруг', 'ли', 'если', 'уже', 'или', 'ни', 'быть', 'был', 'него', 'до', 'вас', 'нибудь', 'опять', 'уж', 'вам', 'ведь', 'там', 'потом', 'себя', 'ничего', 'ей', 'может', 'они', 'тут', 'где', 'есть', 'надо', 'ней', 'для', 'мы', 'тебя', 'их', 'чем', 'была', 'сам', 'чтоб', 'без', 'будто', 'чего', 'раз', 'тоже', 'себе', 'под', 'будет', 'ж', 'тогда', 'кто', 'этот', 'того', 'потому', 'этого', 'какой', 'совсем', 'ним', 'здесь', 'этом', 'один', 'почти', 'мой', 'тем', 'чтобы', 'нее', 'сейчас', 'были', 'куда', 'зачем', 'всех', 'никогда', 'можно', 'при', 'наконец', 'два', 'об', 'другой', 'хоть', 'после', 'над', 'больше', 'тот', 'через', 'эти', 'нас', 'про', 'всего', 'них', 'какая', 'много', 'разве', 'три', 'эту', 'моя', 'впрочем', 'хорошо', 'свою', 'этой', 'перед', 'иногда', 'лучше', 'чуть', 'том', 'нельзя', 'такой', 'им', 'более', 'всегда', 'конечно', 'всю', 'между']


                        # We use NLTK - Loading stop words and removing negative stop words from the list

stop_words = stopwords.words("english")
keep_these_words = [
    "don",
    "don't",
    "ain",
    "aren",
    "aren't",
    "couldn",
    "couldn't",
    "didn",
    "didn't",
    "doesn",
    "doesn't",
    "hadn",
    "hadn't",
    "hasn",
    "hasn't",
    "haven",
    "haven't",
    "isn",
    "isn't",
    "ma",
    "mightn",
    "mightn't",
    "mustn",
    "mustn't",
    "needn",
    "needn't",
    "shan",
    "shan't",
    "no",
    "nor",
    "not",
    "shouldn",
    "shouldn't",
    "wasn",
    "wasn't",
    "weren",
    "weren't",
    "won",
    "won't",
    "wouldn",
    "wouldn't",
]

our_stop_words = stop_words
for word in keep_these_words:
    our_stop_words.remove(word)

print(our_stop_words)

['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y']


                        # Let's review an example before applying it to our DataFrames.

first_paragraph = """1. The Imitation Game\nI propose to consider the question, ""Can machines think?"" This should begin 
with definitions of the meaning of the terms ""machine"" and ""think."" The definitions might be framed so as to reflect so 
far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words ""machine"" and 
""think"" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning 
and the answer to the question, ""Can machines think?"" is to be sought in a statistical survey such as a Gallup poll. But 
this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related 
to it and is expressed in relatively unambiguous words." """

first_paragraph

'1. The Imitation Game\nI propose to consider the question, ""Can machines think?"" This should begin \nwith definitions of the meaning of the terms ""machine"" and ""think."" The definitions might be framed so as to reflect so \nfar as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words ""machine"" and \n""think"" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning \nand the answer to the question, ""Can machines think?"" is to be sought in a statistical survey such as a Gallup poll. But \nthis is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related \nto it and is expressed in relatively unambiguous words." '


                        nltk.download('punkt')
from nltk.tokenize import sent_tokenize

sentences = sent_tokenize(first_paragraph)
sentences

[nltk_data] Downloading package punkt to /home/chris/nltk_data...
[nltk_data]   Package punkt is already up-to-date!

['1.',
 'The Imitation Game\nI propose to consider the question, ""Can machines think?""',
 'This should begin \nwith definitions of the meaning of the terms ""machine"" and ""think.""',
 'The definitions might be framed so as to reflect so \nfar as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words ""machine"" and \n""think"" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning \nand the answer to the question, ""Can machines think?""',
 'is to be sought in a statistical survey such as a Gallup poll.',
 'But \nthis is absurd.',
 'Instead of attempting such a definition I shall replace the question by another, which is closely related \nto it and is expressed in relatively unambiguous words."']


                        # We can check how many sentences we got from the first text we have used for this example:
print("The number of sentences is", len(sentences))

The number of sentences is 7


                        from nltk.tokenize import regexp_tokenize

words_regex = regexp_tokenize(first_paragraph, pattern="\s+", gaps=True)
print(words_regex)

['1.', 'The', 'Imitation', 'Game', 'I', 'propose', 'to', 'consider', 'the', 'question,', '""Can', 'machines', 'think?""', 'This', 'should', 'begin', 'with', 'definitions', 'of', 'the', 'meaning', 'of', 'the', 'terms', '""machine""', 'and', '""think.""', 'The', 'definitions', 'might', 'be', 'framed', 'so', 'as', 'to', 'reflect', 'so', 'far', 'as', 'possible', 'the', 'normal', 'use', 'of', 'the', 'words,', 'but', 'this', 'attitude', 'is', 'dangerous,', 'If', 'the', 'meaning', 'of', 'the', 'words', '""machine""', 'and', '""think""', 'are', 'to', 'be', 'found', 'by', 'examining', 'how', 'they', 'are', 'commonly', 'used', 'it', 'is', 'difficult', 'to', 'escape', 'the', 'conclusion', 'that', 'the', 'meaning', 'and', 'the', 'answer', 'to', 'the', 'question,', '""Can', 'machines', 'think?""', 'is', 'to', 'be', 'sought', 'in', 'a', 'statistical', 'survey', 'such', 'as', 'a', 'Gallup', 'poll.', 'But', 'this', 'is', 'absurd.', 'Instead', 'of', 'attempting', 'such', 'a', 'definition', 'I', 'shall', 'replace', 'the', 'question', 'by', 'another,', 'which', 'is', 'closely', 'related', 'to', 'it', 'and', 'is', 'expressed', 'in', 'relatively', 'unambiguous', 'words."']


                        # How many substrings did we get?
print("The number of substrings is", len(words_regex))

The number of substrings is 133


                        from nltk.tokenize import word_tokenize

words_tokens = word_tokenize(first_paragraph)
print(words_tokens)

['1', '.', 'The', 'Imitation', 'Game', 'I', 'propose', 'to', 'consider', 'the', 'question', ',', '``', "''", 'Can', 'machines', 'think', '?', "''", "''", 'This', 'should', 'begin', 'with', 'definitions', 'of', 'the', 'meaning', 'of', 'the', 'terms', '``', "''", 'machine', "''", "''", 'and', '``', "''", 'think', '.', "''", "''", 'The', 'definitions', 'might', 'be', 'framed', 'so', 'as', 'to', 'reflect', 'so', 'far', 'as', 'possible', 'the', 'normal', 'use', 'of', 'the', 'words', ',', 'but', 'this', 'attitude', 'is', 'dangerous', ',', 'If', 'the', 'meaning', 'of', 'the', 'words', '``', "''", 'machine', "''", "''", 'and', "''", "''", 'think', "''", "''", 'are', 'to', 'be', 'found', 'by', 'examining', 'how', 'they', 'are', 'commonly', 'used', 'it', 'is', 'difficult', 'to', 'escape', 'the', 'conclusion', 'that', 'the', 'meaning', 'and', 'the', 'answer', 'to', 'the', 'question', ',', '``', "''", 'Can', 'machines', 'think', '?', "''", "''", 'is', 'to', 'be', 'sought', 'in', 'a', 'statistical', 'survey', 'such', 'as', 'a', 'Gallup', 'poll', '.', 'But', 'this', 'is', 'absurd', '.', 'Instead', 'of', 'attempting', 'such', 'a', 'definition', 'I', 'shall', 'replace', 'the', 'question', 'by', 'another', ',', 'which', 'is', 'closely', 'related', 'to', 'it', 'and', 'is', 'expressed', 'in', 'relatively', 'unambiguous', 'words', '.', "''"]


                        print("The number of substrings is", len(words_tokens))

The number of substrings is 170


                        from nltk.tokenize import TweetTokenizer

twk = TweetTokenizer()
tweet_tokens = twk.tokenize(first_paragraph)
print(tweet_tokens)

['1', '.', 'The', 'Imitation', 'Game', 'I', 'propose', 'to', 'consider', 'the', 'question', ',', '"', '"', 'Can', 'machines', 'think', '?', '"', '"', 'This', 'should', 'begin', 'with', 'definitions', 'of', 'the', 'meaning', 'of', 'the', 'terms', '"', '"', 'machine', '"', '"', 'and', '"', '"', 'think', '.', '"', '"', 'The', 'definitions', 'might', 'be', 'framed', 'so', 'as', 'to', 'reflect', 'so', 'far', 'as', 'possible', 'the', 'normal', 'use', 'of', 'the', 'words', ',', 'but', 'this', 'attitude', 'is', 'dangerous', ',', 'If', 'the', 'meaning', 'of', 'the', 'words', '"', '"', 'machine', '"', '"', 'and', '"', '"', 'think', '"', '"', 'are', 'to', 'be', 'found', 'by', 'examining', 'how', 'they', 'are', 'commonly', 'used', 'it', 'is', 'difficult', 'to', 'escape', 'the', 'conclusion', 'that', 'the', 'meaning', 'and', 'the', 'answer', 'to', 'the', 'question', ',', '"', '"', 'Can', 'machines', 'think', '?', '"', '"', 'is', 'to', 'be', 'sought', 'in', 'a', 'statistical', 'survey', 'such', 'as', 'a', 'Gallup', 'poll', '.', 'But', 'this', 'is', 'absurd', '.', 'Instead', 'of', 'attempting', 'such', 'a', 'definition', 'I', 'shall', 'replace', 'the', 'question', 'by', 'another', ',', 'which', 'is', 'closely', 'related', 'to', 'it', 'and', 'is', 'expressed', 'in', 'relatively', 'unambiguous', 'words', '.', '"']


                        print("The number of substrings is", len(tweet_tokens))

The number of substrings is 170


                        tweets_df["tweets"].apply(lambda x: sent_tokenize(x))

0       [#followfriday @france_inte @pkuchly57 @milipo...
1       [@lamb2ja hey james!, how odd :/ please call o...
2       [@despiteofficial we had a listen last night :...
3                                  [@97sides congrats :)]
4       [yeaaaah yippppy!!!, my accnt verified rqst ha...
                              ...
9995               [i wanna change my avi but usanele :(]
9996                         [my puppy broke her foot :(]
9997           [where's all the jaebum baby pictures :((]
9998               [but but mr ahmad maslan cooks too :(]
9999    [@eawoman as a hull supporter i am expecting a...
Name: tweets, Length: 10000, dtype: object


                        tweets_df["tweets"].apply(lambda x: regexp_tokenize(x, pattern="\s+", gaps=True))

0       [#followfriday, @france_inte, @pkuchly57, @mil...
1       [@lamb2ja, hey, james!, how, odd, :/, please, ...
2       [@despiteofficial, we, had, a, listen, last, n...
3                                [@97sides, congrats, :)]
4       [yeaaaah, yippppy!!!, my, accnt, verified, rqs...
                              ...
9995        [i, wanna, change, my, avi, but, usanele, :(]
9996                    [my, puppy, broke, her, foot, :(]
9997     [where's, all, the, jaebum, baby, pictures, :((]
9998        [but, but, mr, ahmad, maslan, cooks, too, :(]
9999    [@eawoman, as, a, hull, supporter, i, am, expe...
Name: tweets, Length: 10000, dtype: object


                        tweets_df["tweets"].apply(lambda x: word_tokenize(x))

0       [#, followfriday, @, france_inte, @, pkuchly57...
1       [@, lamb2ja, hey, james, !, how, odd, :, /, pl...
2       [@, despiteofficial, we, had, a, listen, last,...
3                            [@, 97sides, congrats, :, )]
4       [yeaaaah, yippppy, !, !, !, my, accnt, verifie...
                              ...
9995    [i, wan, na, change, my, avi, but, usanele, :, (]
9996                  [my, puppy, broke, her, foot, :, (]
9997    [where, 's, all, the, jaebum, baby, pictures, ...
9998      [but, but, mr, ahmad, maslan, cooks, too, :, (]
9999    [@, eawoman, as, a, hull, supporter, i, am, ex...
Name: tweets, Length: 10000, dtype: object


                        from nltk.tokenize import word_tokenize


def remove_stop_words(tweet):
    tokens = word_tokenize(tweet)
    tweet_with_no_stop_words = [
        token for token in tokens if not token in our_stop_words
    ]
    reformed_tweet = " ".join(tweet_with_no_stop_words)

    return reformed_tweet


tweets_df["clean_tweets"] = tweets_df["clean_tweets"].apply(
    lambda x: remove_stop_words(x)
)

# Saving clean tweets
tweets_df["tweets"] = tweets_df["clean_tweets"]
tweets_df = tweets_df.drop(["clean_tweets"], axis=1)
tweets_df


                        tweets_df["tweets_token"] = tweets_df["tweets"].apply(
    lambda x: regexp_tokenize(x, pattern="\s+", gaps=True)
)
tweets_df


                        tweets_df["tweets_length"] = tweets_df["tweets_token"].apply(lambda x: len(x))
tweets_df


                        plt.figure(figsize=(13, 2))
sns.boxplot(data=tweets_df, x="tweets_length", palette="Reds_r")

<AxesSubplot:xlabel='tweets_length'>


                        plt.figure(figsize=(12, 4))
sns.set_theme(style="white")

sns.histplot(tweets_df, x="tweets_length", color="black")

<AxesSubplot:xlabel='tweets_length', ylabel='Count'>


                        print("Q25=", tweets_df["tweets_length"].quantile(q=0.25))
print("Q50=", tweets_df["tweets_length"].quantile(q=0.5))
print("Q75=", tweets_df["tweets_length"].quantile(q=0.75))

Q25= 6.0
Q50= 8.0
Q75= 12.0


                        tweets_df.tweets_length.describe()

count    10000.00000
mean         9.15850
std          4.05199
min          0.00000
25%          6.00000
50%          8.00000
75%         12.00000
max         40.00000
Name: tweets_length, dtype: float64


                        # Dropping the column tweets_length given that we will not need it for the next steps of our analysis
tweets_df = tweets_df.drop(["tweets_length"], axis=1)


                        from collections import Counter

tokens = Counter()
tweets_df["tweets_token"].apply(tokens.update)

# Printing the count of the tokens
tokens_df = (
    pd.DataFrame.from_dict(tokens, orient="index")
    .reset_index()
    .rename(columns={"index": "token", 0: "count"})
)

tokens_df.sort_values(by="count", ascending=False)


                        plot_tokens_top_30 = tokens_df.sort_values(by="count", ascending=False).head(30)

plt.figure(figsize=(15, 6))
sns.set_theme(style="whitegrid")
ax = sns.barplot(x="token", y="count", data=plot_tokens_top_30)
_ = plt.xticks(rotation=60)


                        from nltk import PorterStemmer

sentence = [
    "I",
    "was",
    "thinking",
    "about",
    "geometry",
    "while",
    "I",
    "was",
    "enjoying",
    "the",
    "best",
    "month",
    "of",
    "winter",
]
porterStemmer = PorterStemmer()

print(
    " Sentence:\n",
    sentence,
    "\n\n",
    "Sentence with stemming:\n",
    " ".join([porterStemmer.stem(word) for word in sentence]),
    "\n",
)

 Sentence:
 ['I', 'was', 'thinking', 'about', 'geometry', 'while', 'I', 'was', 'enjoying', 'the', 'best', 'month', 'of', 'winter']

 Sentence with stemming:
 i wa think about geometri while i wa enjoy the best month of winter


                        porterStemmer = PorterStemmer()
tweets_df["t_stem"] = tweets_df["tweets_token"].apply(
    lambda lst: [porterStemmer.stem(word) for word in lst]
)
tweets_df


                        nltk.download('wordnet')
from nltk.stem import WordNetLemmatizer

sentence = [
    "I",
    "was",
    "thinking",
    "about",
    "geometry",
    "while",
    "I",
    "was",
    "enjoying",
    "the",
    "best",
    "month",
    "of",
    "winter",
]
lemmatizer = WordNetLemmatizer()

print(
    " Sentence:\n",
    sentence,
    "\n\n",
    "Sentence with lemmatizing:\n",
    " ".join([lemmatizer.lemmatize(word) for word in sentence]),
    "\n",
)

[nltk_data] Downloading package wordnet to /home/chris/nltk_data...
[nltk_data]   Package wordnet is already up-to-date!

 Sentence:
 ['I', 'was', 'thinking', 'about', 'geometry', 'while', 'I', 'was', 'enjoying', 'the', 'best', 'month', 'of', 'winter']

 Sentence with lemmatizing:
 I wa thinking about geometry while I wa enjoying the best month of winter


                        # Run this cell to learn more about the tags
# nltk.help.upenn_tagset()


                        nltk.download('averaged_perceptron_tagger')
from nltk.tag import pos_tag

sentence = [
    "I",
    "was",
    "thinking",
    "about",
    "geometry",
    "while",
    "I",
    "was",
    "enjoying",
    "the",
    "best",
    "month",
    "of",
    "winter",
]

nltk.pos_tag(sentence)

[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data]     /home/chris/nltk_data...
[nltk_data]   Package averaged_perceptron_tagger is already up-to-
[nltk_data]       date!

[('I', 'PRP'),
 ('was', 'VBD'),
 ('thinking', 'VBG'),
 ('about', 'IN'),
 ('geometry', 'NN'),
 ('while', 'IN'),
 ('I', 'PRP'),
 ('was', 'VBD'),
 ('enjoying', 'VBG'),
 ('the', 'DT'),
 ('best', 'JJS'),
 ('month', 'NN'),
 ('of', 'IN'),
 ('winter', 'NN')]


                        lmtzr = WordNetLemmatizer()
tweets_df["t_lemma"] = tweets_df["tweets_token"].apply(
    lambda lst: [lmtzr.lemmatize(word) for word in lst]
)
tweets_df.tail()


                        lemmas = Counter()
tweets_df["t_lemma"].apply(lemmas.update)

lemmas_df = (
    pd.DataFrame.from_dict(lemmas, orient="index")
    .reset_index()
    .rename(columns={"index": "lemma", 0: "count"})
)

lemmas_df.sort_values(by="count", ascending=False)


                        plot_lemmas_top_30 = lemmas_df.sort_values(by="count", ascending=False).head(30)

plt.figure(figsize=(15, 6))
sns.set_theme(style="whitegrid")
ax = sns.barplot(x="lemma", y="count", data=plot_lemmas_top_30)
_ = plt.xticks(rotation=60)


                        from nltk import pos_tag

tweets_df["t_postag"] = tweets_df["tweets"].apply(
    lambda v: nltk.pos_tag(nltk.word_tokenize(v))
)
tweets_df.head()


                        tags = Counter()
tweets_df["t_postag"].apply(tags.update)

tags_df = (
    pd.DataFrame.from_dict(tags, orient="index")
    .reset_index()
    .rename(columns={"index": "tag", 0: "count"})
)

tags_df.sort_values(by="count", ascending=False)


                        plot_tags_top_30 = tags_df.sort_values(by="count", ascending=False).head(30)

plt.figure(figsize=(15, 6))
sns.set_theme(style="whitegrid")
ax = sns.barplot(x="tag", y="count", data=plot_tags_top_30)
_ = plt.xticks(rotation=60)


                        from nltk.corpus import wordnet
from nltk.stem import WordNetLemmatizer

lemmatizer = WordNetLemmatizer()


def get_wordnet_pos(treebank_tag):
    if treebank_tag.startswith("J"):
        return wordnet.ADJ
    elif treebank_tag.startswith("V"):
        return wordnet.VERB
    elif treebank_tag.startswith("N"):
        return wordnet.NOUN
    elif treebank_tag.startswith("R"):
        return wordnet.ADV
    else:
        return None


tweets_df["lemma_sentence"] = tweets_df["t_postag"].transform(
    lambda value: " ".join(
        [
            lemmatizer.lemmatize(a[0], pos=get_wordnet_pos(a[1]))
            if get_wordnet_pos(a[1])
            else a[0]
            for a in value
        ]
    )
)
tweets_df


                        Sentence_from_Turing = "The idea behind digital computers may be explained by saying that these machines are intended to carry out any operations which could be done by a human computer."

tokens = nltk.word_tokenize(Sentence_from_Turing)
stemmer = PorterStemmer()
lemmatizer = WordNetLemmatizer()

# Stem and lemmatize
print("{0:30}{1:30}{2:30}".format("Word", "Stemmer", "Lemma\n"))
for word in tokens:
    print(
        "{0:30}{1:30}{2:30}".format(
            word, stemmer.stem(word), lemmatizer.lemmatize(word)
        )
    )

Word                          Stemmer                       Lemma

The                           the                           The
idea                          idea                          idea
behind                        behind                        behind
digital                       digit                         digital
computers                     comput                        computer
may                           may                           may
be                            be                            be
explained                     explain                       explained
by                            by                            by
saying                        say                           saying
that                          that                          that
these                         these                         these
machines                      machin                        machine
are                           are                           are
intended                      intend                        intended
to                            to                            to
carry                         carri                         carry
out                           out                           out
any                           ani                           any
operations                    oper                          operation
which                         which                         which
could                         could                         could
be                            be                            be
done                          done                          done
by                            by                            by
a                             a                             a
human                         human                         human
computer                      comput                        computer
.                             .                             .


                        df = tweets_df[["tweets", "sentiment"]]
df


                        tweets_list = list(tweets_df["tweets"].values)
sentiment = tweets_df["sentiment"].values


                        tweets_list[:10]

['followfriday inte paris top engaged members community week happy face smiley',
 'hey james odd skeptical annoyed undecided uneasy hesitant please call contact centre able assist happy face smiley many thanks',
 'listen last night happy face smiley bleed amazing track scotland',
 'congrats happy face smiley',
 'yeaaaah yippppy accnt verified rqst succeed got blue tick mark fb profile happy face smiley days',
 'one irresistible happy face smileyflipkartfashionfriday',
 'dont like keep lovely customers waiting long hope enjoy happy friday lwwf happy face smiley',
 'second thought theres not enough time dd happy face smiley new shorts entering system sheep must buying',
 'jgh go bayan bye',
 'act mischievousness calling etl layer inhouse warehousing app katamariwell name implies p']


                        # define max features
max_features = 100

# each instance will have 10 features
cv = CountVectorizer(max_features=max_features, ngram_range=(1, 2))


                        matrix_tweets_cv = cv.fit_transform(tweets_list)

# Getting the words
all_words = cv.get_feature_names()

# visualizing the first 50 words
print("First 100 words: \n", all_words[0:100])

First 100 words:
 ['always', 'amp', 'andry', 'andry pouting', 'back', 'birthday', 'cant', 'come', 'could', 'day', 'didnt', 'dont', 'even', 'face', 'face smiley', 'face smileyconfusion', 'feel', 'follow', 'follow back', 'followed', 'friday', 'frown', 'frown sad', 'fun', 'get', 'go', 'going', 'good', 'got', 'great', 'guys', 'happy', 'happy face', 'hey', 'hi', 'home', 'hope', 'ill', 'im', 'ive', 'kik', 'know', 'last', 'let', 'like', 'lol', 'looking', 'lot', 'love', 'lt', 'make', 'miss', 'morning', 'much', 'na', 'need', 'never', 'new', 'next', 'nice', 'night', 'no', 'not', 'oh', 'one', 'people', 'please', 'pouting', 'really', 'right', 'sad', 'sad andry', 'see', 'sleep', 'smiley', 'smileyconfusion', 'snapchat', 'someone', 'soon', 'sorry', 'still', 'thank', 'thanks', 'thats', 'think', 'time', 'today', 'us', 'via', 'wan', 'wan na', 'want', 'week', 'weekend', 'well', 'wish', 'work', 'would', 'yes', 'youre']


                        wordfreq = cv.vocabulary_
wordfreq

{'week': 92,
 'happy': 31,
 'face': 13,
 'smiley': 74,
 'happy face': 32,
 'face smiley': 14,
 'hey': 33,
 'please': 66,
 'thanks': 82,
 'last': 42,
 'night': 60,
 'got': 28,
 'one': 64,
 'dont': 11,
 'like': 44,
 'hope': 36,
 'friday': 20,
 'not': 62,
 'time': 85,
 'new': 57,
 'go': 25,
 'love': 48,
 'follow': 17,
 'amp': 1,
 'back': 4,
 'follow back': 18,
 'know': 41,
 'great': 29,
 'get': 24,
 'today': 86,
 'hi': 34,
 'smileyconfusion': 75,
 'face smileyconfusion': 15,
 'need': 55,
 'ive': 39,
 'fun': 23,
 'lol': 45,
 'thank': 81,
 'us': 87,
 'well': 94,
 'kik': 40,
 'come': 7,
 'lot': 47,
 'see': 72,
 'lt': 49,
 'oh': 63,
 'looking': 46,
 'next': 58,
 'feel': 16,
 'never': 56,
 'good': 27,
 'im': 38,
 'going': 26,
 'would': 97,
 'no': 61,
 'someone': 77,
 'yes': 98,
 'cant': 6,
 'still': 80,
 'day': 9,
 'via': 88,
 'let': 43,
 'youre': 99,
 'weekend': 93,
 'make': 50,
 'really': 68,
 'sleep': 73,
 'birthday': 5,
 'want': 91,
 'miss': 51,
 'morning': 52,
 'thats': 83,
 'ill': 37,
 'right': 69,
 'even': 12,
 'home': 35,
 'soon': 78,
 'wan': 89,
 'could': 8,
 'always': 0,
 'na': 54,
 'wan na': 90,
 'nice': 59,
 'guys': 30,
 'much': 53,
 'work': 96,
 'didnt': 10,
 'people': 65,
 'snapchat': 76,
 'think': 84,
 'wish': 95,
 'sorry': 79,
 'sad': 70,
 'followed': 19,
 'frown': 21,
 'andry': 2,
 'pouting': 67,
 'frown sad': 22,
 'sad andry': 71,
 'andry pouting': 3}


                        matrix_tweets_cv.toarray()[1]

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])


                        print(cv.get_feature_names())

['always', 'amp', 'andry', 'andry pouting', 'back', 'birthday', 'cant', 'come', 'could', 'day', 'didnt', 'dont', 'even', 'face', 'face smiley', 'face smileyconfusion', 'feel', 'follow', 'follow back', 'followed', 'friday', 'frown', 'frown sad', 'fun', 'get', 'go', 'going', 'good', 'got', 'great', 'guys', 'happy', 'happy face', 'hey', 'hi', 'home', 'hope', 'ill', 'im', 'ive', 'kik', 'know', 'last', 'let', 'like', 'lol', 'looking', 'lot', 'love', 'lt', 'make', 'miss', 'morning', 'much', 'na', 'need', 'never', 'new', 'next', 'nice', 'night', 'no', 'not', 'oh', 'one', 'people', 'please', 'pouting', 'really', 'right', 'sad', 'sad andry', 'see', 'sleep', 'smiley', 'smileyconfusion', 'snapchat', 'someone', 'soon', 'sorry', 'still', 'thank', 'thanks', 'thats', 'think', 'time', 'today', 'us', 'via', 'wan', 'wan na', 'want', 'week', 'weekend', 'well', 'wish', 'work', 'would', 'yes', 'youre']


                        len(cv.get_feature_names())

100


                        X = matrix_tweets_cv.toarray()
y = sentiment

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)


                        print(X_train.shape)
print(X_test.shape)

(7000, 100)
(3000, 100)


                        # Model Generation Using Multinomial Naive Bayes
clf = MultinomialNB().fit(X_train, y_train)
predicted = clf.predict(X_test)

print("MultinomialNB Accuracy:", metrics.accuracy_score(y_test, predicted))

MultinomialNB Accuracy: 0.9643333333333334


                        tf = TfidfVectorizer()
text_tf = tf.fit_transform(df["tweets"])


                        X_train, X_test, y_train, y_test = train_test_split(
    text_tf, df["sentiment"], test_size=0.3, random_state=123
)


                        # getting the words
all_words = tf.get_feature_names()

# visualize first 50 words
print("First 50 words: ", all_words[0:50])

First 50 words:  ['aa', 'aaaaaaaaaaa', 'aaaaaaaaaaaaa', 'aaaaaaaaaaaah', 'aaaaaand', 'aaaaages', 'aaaaahhhhhhhh', 'aaaahh', 'aaaahhh', 'aaahhh', 'aah', 'aahhhh', 'aaj', 'aameenall', 'aameenhappy', 'aamir', 'aapke', 'aaron', 'aarwwi', 'aasramany', 'aayegi', 'ab', 'abandoned', 'abandoning', 'abbeytaylor', 'abby', 'abbycan', 'abbymill', 'abes', 'abhi', 'abi', 'abit', 'abla', 'able', 'abligaverins', 'aboard', 'abouty', 'abp', 'abroad', 'abrupt', 'abs', 'abscess', 'absent', 'absolute', 'absolutely', 'abstinence', 'abt', 'abu', 'abudhabi', 'abusive']


                        print(tf.vocabulary_)

{'followfriday': 4009, 'inte': 5513, 'paris': 8309, 'top': 11824, 'engaged': 3417, 'members': 7087, 'community': 2211, 'week': 12617, 'happy': 4792, 'face': 3658, 'smiley': 10448, 'hey': 4971, 'james': 5682, 'odd': 7957, 'skeptical': 10364, 'annoyed': 454, 'undecided': 12140, 'uneasy': 12149, 'hesitant': 4967, 'please': 8675, 'call': 1676, 'contact': 2298, 'centre': 1818, 'able': 33, 'assist': 652, 'many': 6891, 'thanks': 11525, 'listen': 6494, 'last': 6281, 'night': 7750, 'bleed': 1264, 'amazing': 361, 'track': 11872, 'scotland': 9909, 'congrats': 2268, 'yeaaaah': 13014, 'yippppy': 13071, 'accnt': 63, 'verified': 12347, 'rqst': 9690, 'succeed': 11097, 'got': 4502, 'blue': 1300, 'tick': 11681, 'mark': 6915, 'fb': 3791, 'profile': 9010, 'days': 2677, 'one': 8056, 'irresistible': 5577, 'smileyflipkartfashionfriday': 10497, 'dont': 3070, 'like': 6440, 'keep': 5991, 'lovely': 6657, 'customers': 2560, 'waiting': 12498, 'long': 6583, 'hope': 5099, 'enjoy': 3427, 'friday': 4112, 'lwwf': 6739, 'second': 9950, 'thought': 11632, 'theres': 11576, 'not': 7852, 'enough': 3432, 'time': 11700, 'dd': 2690, 'new': 7684, 'shorts': 10223, 'entering': 3439, 'system': 11289, 'sheep': 10150, 'must': 7495, 'buying': 1636, 'jgh': 5762, 'go': 4432, 'bayan': 960, 'bye': 1643, 'act': 89, 'mischievousness': 7240, 'calling': 1679, 'etl': 3517, 'layer': 6315, 'inhouse': 5456, 'warehousing': 12537, 'app': 525, 'katamariwell': 5959, 'name': 7563, 'implies': 5364, 'gouv': 4509, 'influencers': 5440, 'wouldnt': 12893, 'love': 6646, 'bigjuicyselfies': 1172, 'follow': 4004, 'amp': 390, 'back': 800, 'perfect': 8457, 'already': 335, 'know': 6142, 'whats': 12685, 'great': 4551, 'opportunity': 8105, 'junior': 5892, 'triathletes': 11933, 'aged': 190, 'gatorade': 4274, 'series': 10033, 'get': 4331, 'entries': 3453, 'laying': 6316, 'greetings': 4565, 'card': 1733, 'range': 9227, 'print': 8969, 'today': 11754, 'job': 5791, 'friends': 4131, 'lunch': 6720, 'yummmm': 13145, 'smileynostalgia': 10531, 'tbs': 11402, 'ku': 6180, 'id': 5283, 'conflict': 2255, 'help': 4941, 'heres': 4960, 'screenshot': 9921, 'working': 12861, 'hi': 4976, 'liv': 6509, 'smileyconfusion': 10465, 'hello': 4937, 'need': 7640, 'something': 10689, 'fm': 3985, 'twitter': 12050, 'sure': 11195, 'thing': 11593, 'dm': 3013, 'fle': 3949, 'followers': 4008, 'ive': 5640, 'heard': 4888, 'four': 4072, 'seasons': 9944, 'pretty': 8945, 'dope': 3081, 'penthouse': 8444, 'obvs': 7946, 'gobigorgohomehave': 4439, 'fun': 4192, 'yall': 12991, 'yeah': 13015, 'suppose': 11187, 'lol': 6561, 'chat': 1901, 'bit': 1221, 'youth': 13117, 'opportunities': 8104, 'gtgt': 4622, 'nailpolishmediumskintonekissmark': 7550, 'smileyconfusionconfusionconfusion': 10468, 'havent': 4847, 'seen': 9972, 'years': 13020, 'arrow': 602, 'thank': 11519, 'rest': 9493, 'goes': 4446, 'quickly': 9167, 'bed': 1032, 'music': 7485, 'fix': 3930, 'dream': 3128, 'spiritual': 10826, 'ritual': 9588, 'festival': 3841, 'npalconfusionbeginning': 7890, 'lineup': 6474, 'smileyit': 10510, 'left': 6354, 'yconfusionsee': 13010, 'sarah': 9825, 'send': 10007, 'us': 12257, 'email': 3354, 'bitsycom': 1233, 'well': 12643, 'asap': 616, 'lols': 6570, 'kik': 6051, 'hatessuce': 4838, 'kikme': 6056, 'lgbt': 6405, 'tinder': 11711, 'nsfw': 7894, 'akua': 271, 'cumshot': 2536, 'come': 2185, 'house': 5154, 'smileyconfusionconfusionconfusionconfusion': 10469, 'nsnsupplements': 7895, 'effective': 3285, 'press': 8936, 'release': 9402, 'distribution': 2991, 'results': 9501, 'link': 6477, 'removed': 9428, 'pressrelease': 8937, 'newsdistribution': 7695, 'bam': 871, 'bestfriend': 1120, 'loves': 6665, 'lot': 6621, 'see': 9961, 'warsaw': 12547, 'lt': 6689, 'everyone': 3546, 'watch': 12557, 'documentary': 3027, 'earthlings': 3244, 'youtube': 13122, 'supports': 11186, 'buuuuuuuut': 1633, 'oh': 7983, 'looking': 6594, 'forward': 4062, 'visiting': 12427, 'next': 7702, 'letsgetmessy': 6394, 'jo': 5790, 'makes': 6830, 'feel': 3810, 'better': 1133, 'never': 7682, 'nor': 7841, 'anyone': 491, 'kpop': 6168, 'flesh': 3951, 'good': 4465, 'girl': 4382, 'best': 1117, 'wishes': 12788, 'kimimi': 6075, 'reason': 9303, 'epic': 3466, 'soundtrack': 10759, 'shout': 10235, 'added': 110, 'video': 12380, 'playlist': 8668, 'im': 5339, 'twitch': 12047, 'going': 4451, 'league': 6332, 'would': 12892, 'dear': 2703, 'jordan': 5826, 'ana': 400, 'okay': 8000, 'fake': 3709, 'gameplays': 4239, 'wink': 12773, 'smirk': 10585, 'haha': 4696, 'kidding': 6044, 'stuff': 11062, 'aygur': 772, 'exactly': 3564, 'product': 9005, 'line': 6471, 'etsy': 3519, 'shop': 10212, 'check': 1913, 'boxroomcrafts': 1410, 'vacation': 12294, 'rechargeable': 9320, 'normally': 7845, 'comes': 2188, 'charger': 1886, 'buy': 1634, 'espana': 3498, 'menthe': 7106, 'inter': 5521, 'shes': 10165, 'asleep': 637, 'no': 7793, 'talk': 11332, 'sooo': 10716, 'someone': 10686, 'text': 11496, 'yes': 13046, 'bet': 1127, 'hell': 4934, 'fit': 3920, 'hearing': 4890, 'speech': 10805, 'pity': 8623, 'green': 4562, 'gardens': 4264, 'midnight': 7175, 'sun': 11143, 'beautiful': 1014, 'canals': 1705, 'dasvidaniya': 2655, 'till': 11698, 'visithappy': 12426, 'scouting': 9915, 'sg': 10091, 'future': 4209, 'wlan': 12804, 'pros': 9046, 'conference': 2247, 'asia': 625, 'change': 1862, 'lollipop': 6566, 'nez': 7705, 'agnezmo': 199, 'big': 1168, 'oley': 8025, 'mama': 6847, 'stand': 10892, 'stronger': 11039, 'kjkkndr': 6121, 'god': 4440, 'misty': 7261, 'baby': 789, 'cute': 2562, 'woohoo': 12842, 'cant': 1721, 'wait': 12494, 'signed': 10290, 'yet': 13059, 'still': 10975, 'thinking': 11599, 'mka': 7276, 'liam': 6407, 'access': 59, 'welcome': 12637, 'stats': 10932, 'day': 2671, 'arrived': 601, 'follower': 4007, 'unfollowers': 12160, 'via': 12360, 'shouldnt': 10232, 'surprisedyou': 11202, 'figure': 3865, 'fr': 4077, 'happybirthdayemilybett': 4793, 'wishing': 12789, 'beautifulsweettalentedamazing': 1018, 'plans': 8650, 'drain': 3118, 'smileyembarrassed': 10492, 'blushing': 1307, 'fahupdates': 3699, 'ta': 11290, 'timezones': 11705, 'parents': 8307, 'proud': 9051, 'least': 6340, 'maybe': 6990, 'sometimes': 10692, 'grades': 4521, 'al': 272, 'grande': 4531, 'manilabro': 6877, 'chosen': 1998, 'let': 6390, 'youre': 13110, 'aroundat': 595, 'side': 10275, 'world': 12873, 'eh': 3295, 'take': 11315, 'care': 1739, 'laney': 6255, 'finally': 3884, 'fucking': 4176, 'weekend': 12618, 'real': 9286, 'joined': 5806, 'hushedcallwithfraydoe': 5243, 'gift': 4360, 'fraydoe': 4087, 'yeahhh': 13017, 'make': 6827, 'smileyconfusionconfusion': 10467, 'hushedpinwithsammy': 5244, 'event': 3534, 'might': 7177, 'luv': 6731, 'really': 9298, 'appreciate': 544, 'share': 10123, 'around': 594, 'wow': 12897, 'tom': 11780, 'gym': 4677, 'monday': 7329, 'likes': 6451, 'invite': 5553, 'join': 5805, 'scope': 9902, 'influencer': 5439, 'nudes': 7900, 'sleep': 10395, 'smileyconfusionconfusionconfusionconfusionconfusionconfusionconfusionconfusion': 10473, 'birthday': 1208, 'jp': 5847, 'hideo': 4982, 'want': 12529, 'tshirts': 11978, 'cool': 2346, 'haw': 4850, 'phela': 8514, 'mom': 7315, 'obviously': 7945, 'prince': 8965, 'charming': 1897, 'stage': 10883, 'luck': 6701, 'das': 2651, 'tylers': 12067, 'hipster': 5022, 'glasses': 4411, 'marty': 6933, 'glad': 4406, 'joining': 5807, 'done': 3057, 'afternoon': 174, 'lets': 6392, 'read': 9279, 'kahfi': 5914, 'finish': 3903, 'ohmyg': 7992, 'yaya': 13004, 'dub': 3174, 'stalk': 10886, 'ig': 5306, 'gondooo': 4462, 'moo': 7348, 'tologooo': 11779, 'become': 1030, 'details': 2844, 'robertkelly': 9616, 'spaniel': 10777, 'nelson': 7660, 'zzz': 13207, 'xx': 12966, 'physiotherapy': 8552, 'hashtag': 4826, 'custom': 2558, 'flexedbiceps': 3954, 'monica': 7332, 'miss': 7244, 'sounds': 10758, 'morning': 7365, 'thats': 11542, 'takes': 11320, 'definitely': 2751, 'try': 11972, 'tonight': 11804, 'took': 11815, 'advice': 149, 'treviso': 11929, 'concert': 2240, 'city': 2044, 'country': 2402, 'ill': 5328, 'start': 10911, 'fine': 3894, 'gorgeous': 4498, 'friend': 4127, 'xohappy': 12955, 'oven': 8198, 'roasted': 9610, 'garlic': 4265, 'olive': 8026, 'oil': 7995, 'dried': 3143, 'tomatoes': 11783, 'basil': 935, 'century': 1822, 'tuna': 12003, 'business': 1618, 'thebunkerjl': 11548, 'right': 9560, 'atchya': 660, 'doesnt': 3031, 'even': 3532, 'almost': 328, 'chance': 1857, 'arvsgt': 615, 'president': 8935, 'cheers': 1923, 'po': 8712, 'ice': 5273, 'cream': 2463, 'agree': 204, 'hehehehe': 4927, 'point': 8722, 'stay': 10935, 'home': 5068, 'lafontpresse': 6227, 'soon': 10712, 'promise': 9022, 'web': 12601, 'whatsapp': 12686, 'volta': 12447, 'funcionar': 4193, 'com': 2180, 'iphone': 5562, 'jailbroken': 5675, 'plan': 8640, 'watching': 12561, 'laterconfusion': 6290, 'mins': 7228, 'leia': 6369, 'appears': 533, 'hologram': 5065, 'rd': 9266, 'message': 7129, 'obi': 7938, 'wan': 12526, 'sits': 10343, 'luke': 6714, 'ucl': 12086, 'arsenal': 604, 'small': 10433, 'team': 11417, 'passing': 8339, 'locomotive': 6552, 'dewsbury': 2862, 'railway': 9195, 'station': 10930, 'dewconfusion': 2861, 'west': 12659, 'yorkshireconfusion': 13094, 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'wife': 12742, 'kids': 6048, 'poor': 8751, 'chicks': 1952, 'guide': 4641, 'zonzofox': 13197, 'bhaiya': 1149, 'brother': 1513, 'lucky': 6705, 'dog': 3033, 'patty': 8367, 'elaborate': 3316, 'kuching': 6183, 'rate': 9249, 'merdeka': 7119, 'palace': 8265, 'hotel': 5137, 'suites': 11127, 'plusmiles': 8706, 'service': 10045, 'hahahaa': 4701, 'marm': 6919, 'mean': 7023, 'nex': 7701, 'safe': 9762, 'gwd': 4672, 'okok': 8013, 'idiot': 5292, 'chaerin': 1837, 'unnie': 12194, 'viable': 12361, 'alternative': 342, 'nowadays': 7875, 'pass': 8336, 'ip': 5560, 'tombow': 11784, 'abt': 46, 'friyay': 4143, 'xxx': 12968, 'smug': 10602, 'marrickville': 6923, 'public': 9077, 'ten': 11468, 'ago': 200, 'eighteen': 3305, 'auvssscr': 713, 'ncaaseason': 7620, 'kills': 6070, 'slow': 10424, 'trying': 11973, 'popsicles': 8762, 'soft': 10660, 'melt': 7084, 'mouth': 7395, 'thankyouuu': 11533, 'dianna': 2882, 'ngga': 7712, 'usah': 12261, 'dipikirin': 2927, 'elah': 3317, 'easily': 3247, 'whos': 12724, 'entp': 3449, 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1685, 'constantly': 2290, 'messaging': 7132, 'announce': 452, 'filbarbarian': 3871, 'beer': 1036, 'talks': 11339, 'arms': 590, 'testicle': 11491, 'light': 6435, 'social': 10644, 'katerina': 5963, 'maniataki': 6874, 'ahh': 214, 'alright': 337, 'worthwhile': 12887, 'judging': 5860, 'tech': 11441, 'windows': 12764, 'asking': 634, 'stupid': 11067, 'loving': 6674, 'plugin': 8703, 'bass': 940, 'slap': 10387, 'doors': 3080, 'vipgeneralconfusion': 12413, 'seats': 9947, 'early': 3235, 'actshappy': 98, 'london': 6573, 'babyhappy': 792, 'toptravelcentar': 11832, 'ttctop': 11983, 'lux': 6732, 'luxurytravel': 6737, 'beograd': 1102, 'srbija': 10866, 'putovanja': 9126, 'wendy': 12652, 'provide': 9056, 'fresh': 4105, 'drainage': 3119, 'homebound': 5070, 'hahahays': 4712, 'yeeeeah': 13022, 'moar': 7288, 'kittehs': 6115, 'incoming': 5400, 'towers': 11861, 'yippeee': 13070, 'scrummy': 9931, 'recipes': 9323, 'bio': 1194, 'moved': 7398, 'mcpe': 7018, 'vainglory': 12300, 'driver': 3151, 'lilydale': 6460, 'fss': 4165, 'raise': 9202, 'magicalmysterytour': 6798, 'chek': 1928, 'rules': 9711, 'weebly': 12614, 'donetsk': 3058, 'smirkconfusionconfusion': 10587, 'earth': 3242, 'ros': 9662, 'followingif': 4012, 'printed': 8970, 'personalised': 8475, 'wrap': 12903, 'cardsstationery': 1738, 'adrian': 137, 'parcel': 8304, 'tuesday': 11992, 'pris': 8976, 'wz': 12937, 'pattern': 8366, 'cuttingmaking': 2570, 'buttonhole': 1628, 'finishing': 3905, 'designer': 2828, 'famous': 3731, 'clients': 2089, 'keeps': 5995, 'alive': 303, 'trial': 11931, 'spm': 10830, 'dinooo': 2926, 'cardio': 1736, 'steak': 10940, 'cue': 2531, 'photo': 8532, 'laptop': 6271, 'putting': 9128, 'guinea': 4644, 'pigs': 8580, 'smileylt': 10522, 'bestfriendsfriends': 1123, 'salamat': 9779, 'mga': 7152, 'naggreet': 7541, 'guise': 4646, 'godbless': 4441, 'crush': 2510, 'apple': 537, 'eye': 3638, 'ga': 4220, 'models': 7302, 'deserve': 2823, 'charles': 1892, 'workhard': 12858, 'instagram': 5498, 'ys': 13133, 'aiden': 226, 'forritwah': 4056, 'teabread': 11412, 'baconbutter': 819, 'jay': 5712, 'afang': 156, 'soup': 10760, 'semo': 10006, 'brb': 1449, 'forced': 4034, 'lbloggers': 6322, 'pbloggers': 8390, 'bookreview': 1358, 'toddlerlife': 11764, 'tato': 11376, 'bulat': 1572, 'discussing': 2964, 'shared': 10124, 'suggestion': 11119, 'concerned': 2239, 'snake': 10605, 'performed': 8463, 'con': 2235, 'todayyyyy': 11762, 'max': 6987, 'gaza': 4282, 'retweet': 9512, 'delsigthis': 2779, 'logging': 6556, 'bbb': 965, 'tried': 11938, 'peacefully': 8406, 'legal': 6358, 'age': 189, 'shant': 10119, 'ditching': 2998, 'toryhappy': 11841, 'bajrangibhaijaanhighestweek': 847, 'sokay': 10668, 'andythank': 420, 'youand': 13097, 'return': 9509, 'tuitutil': 11994, 'bud': 1550, 'ass': 641, 'learn': 6335, 'takeaways': 11316, 'instead': 5504, 'hr': 5171, 'genial': 4312, 'competition': 2221, 'naruto': 7585, 'yosh': 13095, 'procrastinating': 9001, 'plus': 8704, 'sorting': 10742, 'boll': 1333, 'telling': 11463, 'kfc': 6018, 'itunes': 5636, 'dedicatedfan': 2731, 'purpleheartpurpleheartpurpleheartpurpleheart': 9114, 'daft': 2596, 'teething': 11455, 'troubles': 11957, 'huxley': 5247, 'basket': 937, 'ben': 1086, 'sent': 10018, 'gamer': 4240, 'trainfollow': 11891, 'meretweet': 7121, 'thisfollow': 11607, 'retweetedgain': 9514, 'active': 93, 'distance': 2987, 'propose': 9043, 'suitable': 11123, 'comment': 2198, 'alternatives': 344, 'final': 3881, 'tgifhappy': 11504, 'stockholm': 10979, 'zack': 13163, 'destroying': 2841, 'heel': 4922, 'claws': 2069, 'moviewill': 7405, 'blonde': 1291, 'box': 1408, 'cheerios': 1922, 'seeds': 9963, 'problems': 8993, 'cutest': 2563, 'ffback': 3849, 'cooder': 2339, 'spotify': 10845, 'weve': 12664, 'vc': 12326, 'careers': 1741, 'tgp': 11508, 'races': 9182, 'average': 719, 'joes': 5798, 'bluejays': 1302, 'vinylbear': 12410, 'pals': 8269, 'furbabys': 4203, 'luffs': 6712, 'mega': 7056, 'retail': 9503, 'launch': 6302, 'whsmith': 12726, 'ps': 9062, 'keeping': 5993, 'purpleheart': 9113, 'shannon': 10118, 'closing': 2111, 'redecorate': 9346, 'bob': 1321, 'ellie': 3340, 'mairi': 6821, 'workouts': 12867, 'impairs': 5360, 'uggghhhh': 12092, 'dam': 2606, 'dun': 3198, 'eczema': 3259, 'sufferers': 11111, 'umji': 12119, 'ndee': 7625, 'bargainsuk': 914, 'pleasure': 8685, 'publilius': 9079, 'syrus': 11288, 'fear': 3800, 'death': 2706, 'dreaded': 3127, 'fell': 3824, 'fuk': 4183, 'unblock': 12134, 'youll': 13102, 'manually': 6886, 'tweak': 12022, 'php': 8544, 'fall': 3715, 'oomf': 8076, 'pippa': 8605, 'hschool': 5176, 'bus': 1612, 'uses': 12270, 'cardi': 1735, 'everyday': 3545, 'everytime': 3554, 'laughingsmiling': 6300, 'hk': 5036, 'whyd': 12728, 'acorn': 86, 'originally': 8144, 'apart': 507, 'cpuconfusion': 2437, 'considerably': 2284, 'advanced': 144, 'onair': 8052, 'bay': 959, 'hold': 5055, 'river': 9589, 'rescue': 9469, 'mutt': 7504, 'wondered': 12829, 'confirm': 2252, 'delivery': 2776, 'sanjaya': 9817, 'switched': 11266, 'lap': 6268, 'optimized': 8112, 'lu': 6697, 'straight': 11009, 'omg': 8044, 'tweetofthedecade': 12034, 'class': 2062, 'happiest': 4787, 'fridays': 4124, 'bbmme': 971, 'pindfea': 8592, 'bbm': 969, 'pin': 8590, 'bbmpin': 972, 'addmeonbbm': 119, 'addme': 118, 'todays': 11758, 'normal': 7844, 'menu': 7110, 'married': 6924, 'glenn': 4416, 'decker': 2728, 'height': 4929, 'sculptors': 9932, 'pillar': 8584, 'storegood': 11000, 'released': 9403, 'ti': 11679, 'dota': 3094, 'image': 5342, 'mentions': 7109, 'nudge': 7901, 'spot': 10844, 'tasty': 11373, 'plant': 8651, 'hilly': 5007, 'cycle': 2576, 'england': 3421, 'scotlandismassive': 9910, 'gen': 4302, 'vikk': 12398, 'fna': 3989, 'mombasa': 7316, 'feels': 3817, 'tukutanemombasa': 11995, 'reasonstovisitmombasa': 9305, 'karibumombasa': 5948, 'hanbin': 4763, 'dseems': 3167, 'giving': 4401, 'banner': 897, 'certainly': 1830, 'goosnight': 4496, 'kindly': 6086, 'familiar': 3724, 'jealous': 5727, 'sound': 10755, 'hitting': 5033, 'tent': 11475, 'yea': 13013, 'cozy': 2433, 'chilled': 1960, 'phenomenal': 8515, 'collab': 2153, 'gave': 4276, 'birth': 1206, 'facewithtearsofjoy': 3675, 'behaves': 1054, 'monster': 7337, 'doesntconfusion': 3032, 'spree': 10853, 'tank': 11356, 'outstanding': 8192, 'donation': 3056, 'contestkiduniya': 2311, 'mfundo': 7151, 'balboa': 857, 'oche': 7953, 'hun': 5220, 'inner': 5469, 'nerd': 7669, 'tamed': 11345, 'insidious': 5486, 'logic': 6557, 'math': 6973, 'channel': 1868, 'continues': 2316, 'doubt': 3097, 'itconfusion': 5615, 'subs': 11088, 'teoh': 11478, 'forgiven': 4047, 'wonderfuls': 12832, 'mannerfuls': 6879, 'yhoooooo': 13065, 'ngi': 7713, 'mood': 7349, 'pushing': 9121, 'limits': 6466, 'obakeng': 7935, 'stop': 10993, 'goats': 4438, 'alhamdullilah': 296, 'talkhappy': 11335, 'pebble': 8415, 'engrossed': 3424, 'bing': 1191, 'screaming': 9919, 'whole': 12720, 'wide': 12738, 'globeshowingamericasglobeshowingamericasredheartredheartredheart': 4419, 'anguishedface': 437, 'wat': 12556, 'muahhh': 7436, 'smileymay': 10525, 'bless': 1269, 'pausetime': 8371, 'cars': 1766, 'drifting': 3144, 'loose': 6601, 'scopidf': 9903, 'pay': 8374, 'campaign': 1697, 'kickstarter': 6042, 'article': 606, 'absolute': 43, 'jenna': 5740, 'bellybutton': 1078, 'innie': 5470, 'outie': 8185, 'sam': 9790, 'delish': 2773, 'ranges': 9228, 'supermarkets': 11175, 'joselito': 5830, 'freya': 4109, 'nth': 7898, 'latepost': 6287, 'lupet': 6725, 'mo': 7286, 'eric': 3477, 'askaman': 630, 'helpful': 4944, 'alternatively': 343, 'webz': 12608, 'oops': 8083, 'theyll': 11587, 'realise': 9287, 'anymore': 488, 'carmel': 1753, 'kko': 6124, 'decisions': 2726, 'nicapapa': 7729, 'matt': 6976, 'probs': 8995, 'honestly': 5084, 'explain': 3614, 'relationship': 9393, 'pick': 8558, 'employers': 3385, 'wellhappy': 12645, 'tessnzachcute': 11488, 'paperboys': 8294, 'papers': 8295, 'honest': 5083, 'reassured': 9306, 'personal': 8474, 'guysss': 4668, 'happen': 4780, 'mubank': 7437, 'dongwoos': 3064, 'bright': 1476, 'tommorow': 11791, 'newyork': 7700, 'magic': 6795, 'lollll': 6568, 'twinx': 12044, 'path': 8352, 'firmansyahbl': 3914, 'usual': 12275, 'procedure': 8996, 'grim': 4577, 'fandango': 3739, 'ordinary': 8130, 'extraordinary': 3629, 'bos': 1383, 'birmingham': 1205, 'oracle': 8117, 'samosas': 9799, 'fireball': 3911, 'shoe': 10204, 'serves': 10044, 'sushi': 11214, 'shoeshi': 10206, 'flat': 3941, 'lymond': 6748, 'philippa': 8519, 'novel': 7873, 'tara': 11361, 'aur': 699, 'han': 4760, 'imran': 5374, 'khan': 6026, 'agaaaain': 182, 'sir': 10326, 'doli': 3041, 'siregar': 10328, 'ninh': 7770, 'size': 10352, 'gems': 4301, 'geekiest': 4292, 'geeks': 4293, 'wallets': 12521, 'request': 9464, 'persons': 8479, 'covering': 2427, 'rally': 9211, 'rotate': 9672, 'sucks': 11103, 'directionhappy': 2935, 'driving': 3154, 'eek': 3283, 'red': 9344, 'suitcasebeijing': 11125, 'china': 1966, 'meni': 7099, 'tebrik': 11440, 'etdi': 3512, 'smileyconfusioncongratulations': 10482, 'forevergrowingheart': 4043, 'rod': 9631, 'tame': 11344, 'embracing': 3360, 'actor': 95, 'aplomb': 517, 'foreveralone': 4042, 'jobs': 5794, 'mysummer': 7525, 'hahahaha': 4705, 'wear': 12590, 'uniform': 12171, 'evil': 3558, 'owwwwchoo': 8225, 'chweet': 2023, 'shorthaired': 10220, 'oscar': 8153, 'realized': 9295, 'harmony': 4812, 'judge': 5859, 'denerivery': 2791, 'kiksexting': 6062, 'kikkomansabor': 6055, 'killer': 6066, 'henessydiaries': 4955, 'journey': 5837, 'band': 879, 'convo': 2337, 'vault': 12322, 'expanding': 3595, 'vinny': 12408, 'money': 7330, 'hahahahaha': 4706, 'cents': 1821, 'repay': 9440, 'debt': 2718, 'smileyi': 10508, 'kirmizi': 6099, 'evet': 3556, 'wifi': 12744, 'lifestyle': 6430, 'qatarday': 9138, 'sunwithfacesunwithfacesunwithface': 11162, 'innovate': 5472, 'talked': 11334, 'volunteering': 12450, 'saran': 9828, 'drama': 3122, 'genre': 4314, 'romance': 9650, 'comedyhappy': 2187, 'leanneriner': 6334, 'porno': 8770, 'weloveyounamjoon': 12647, 'homey': 5077, 'kenya': 6002, 'emotional': 3379, 'roller': 9646, 'coaster': 2124, 'aspect': 639, 'najam': 7554, 'sethis': 10054, 'confession': 2248, 'ad': 105, 'pricelessantique': 8958, 'takesonetoknowone': 11321, 'extra': 3628, 'ucount': 12087, 'ji': 5768, 'books': 1359, 'turkish': 12010, 'knew': 6135, 'crap': 2451, 'burn': 1604, 'airways': 247, 'trusting': 11970, 'airline': 241, 'deliver': 2774, 'sexy': 10080, 'backhandindexpointingleftbeatingheart': 805, 'yello': 13035, 'race': 9181, 'gail': 4226, 'yael': 12986, 'lesson': 6387, 'en': 3395, 'mis': 7239, 'manos': 6881, 'holding': 5057, 'hands': 4770, 'managers': 6863, 'prettiest': 8944, 'readers': 9281, 'rose': 9664, 'dnt': 3018, 'minho': 7218, 'ideal': 5287, 'type': 12070, 'weekly': 12625, 'idol': 5294, 'pose': 8779, 'gtlt': 4628, 'shortlist': 10221, 'dominion': 3050, 'picnic': 8563, 'tmrw': 11746, 'nobody': 7795, 'jummamubarak': 5880, 'shower': 10245, 'shalwarkameez': 10110, 'itter': 5634, 'offer': 7968, 'jummaprayer': 5882, 'aus': 701, 'af': 155, 'display': 2982, 'enabled': 3397, 'company': 2214, 'peeps': 8425, 'tweeps': 12026, 'folow': 4017, 'grace': 4517, 'ohhh': 7989, 'teaser': 11436, 'airecs': 237, 'acid': 81, 'mouse': 7394, 'ep': 3463, 'including': 5399, 'robin': 9618, 'rough': 9676, 'control': 2325, 'remixes': 9425, 'encourage': 3402, 'changing': 1865, 'oe': 7962, 'rts': 9697, 'faves': 3779, 'toss': 11843, 'ladies': 6219, 'asmita': 638, 'welcomesheep': 12639, 'library': 6416, 'stores': 11001, 'mr': 7412, 'climbed': 2095, 'cuddle': 2527, 'jilla': 5770, 'opening': 8091, 'headlining': 4876, 'jumma': 5878, 'mubarik': 7439, 'spent': 10812, 'total': 11845, 'congratz': 2272, 'bitches': 1223, 'contribution': 2323, 'yuppiieeehappy': 13157, 'smileyalienthoughthappyalien': 10450, 'crowd': 2503, 'loudest': 6627, 'garydid': 4268, 'particular': 8319, 'attraction': 686, 'date': 2658, 'supprt': 11190, 'zyrophobia': 13205, 'savage': 9849, 'eshop': 3494, 'cleanse': 2073, 'scam': 9872, 'ridden': 9553, 'vyapam': 12471, 'renamedwhat': 9430, 'tongue': 11801, 'sticking': 10970, 'cheeky': 1919, 'playful': 8665, 'blowing': 1297, 'raspberryd': 9244, 'pleased': 8676, 'waves': 12571, 'couch': 2386, 'eight': 3304, 'wondering': 12833, 'dodged': 3028, 'explanation': 3616, 'proving': 9057, 'grow': 4599, 'bag': 835, 'sanza': 9821, 'truman': 11968, 'places': 8637, 'coloured': 2178, 'aggregates': 195, 'tend': 11469, 'yaa': 12975, 'slr': 10428, 'som': 10681, 'honoured': 5093, 'hehehe': 4926, 'view': 12391, 'geary': 4289, 'elsehappy': 3347, 'explorers': 3619, 'bird': 1202, 'wayanadan': 12574, 'forests': 4040, 'smileywayanadphoto': 10562, 'srijith': 10867, 'vvisit': 12469, 'website': 12605, 'whispered': 12709, 'lying': 6745, 'pokemon': 8726, 'flipkartfashionfridayhey': 3963, 'dazzle': 2683, 'urself': 12254, 'double': 3096, 'flared': 3938, 'black': 1242, 'forideas': 4050, 'views': 12395, 'browse': 1522, 'bored': 1376, 'becoming': 1031, 'female': 3828, 'tour': 11853, 'manager': 6862, 'delve': 2782, 'muchhh': 7447, 'tmr': 11745, 'breakfast': 1455, 'holidays': 5061, 'gl': 4403, 'tonights': 11807, 'count': 2396, 'smileyconfusionconfusionhappy': 10480, 'litey': 6504, 'manuella': 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2149, 'sausage': 9847, 'adios': 126, 'messenger': 7134, 'shittier': 10193, 'leno': 6378, 'identity': 5291, 'crisis': 2490, 'roommates': 9660, 'knocked': 6140, 'nighter': 7752, 'flew': 3953, 'visits': 12428, 'wetherspoons': 12663, 'pubs': 9081, 'police': 8731, 'lisaherring': 6490, 'ebony': 3255, 'polka': 8738, 'loudlycryingfaceloudlycryingface': 6631, 'ndi': 7626, 'leftovers': 6355, 'grahampatrick': 4525, 'owns': 8222, 'ruined': 9707, 'walnut': 12525, 'whips': 12708, 'boah': 1315, 'mady': 6787, 'momentnot': 7319, 'aminormalyet': 384, 'poorly': 8754, 'pjs': 8632, 'groaning': 4588, 'stroya': 11041, 'nou': 7871, 'ken': 5998, 'saras': 9830, 'accident': 61, 'manage': 6859, 'describe': 2821, 'prydz': 9060, 'sisterinlaw': 10337, 'installed': 5501, 'seat': 9945, 'rearended': 9302, 'everyones': 3549, 'boobs': 1352, 'stairs': 10885, 'childhood': 1956, 'toothsensitivity': 11820, 'factor': 3689, 'shem': 10159, 'awell': 740, 'weekendofmadness': 12620, 'tropicaldrinkcocktailglasstropicaldrink': 11955, 'cb': 1798, 'threatened': 11640, 'dancer': 2621, 'noi': 7805, 'choregrapherbut': 1991, 'poutingwhyyyyyyyyyyyy': 8875, 'happening': 4784, 'existed': 3588, 'messaged': 7130, 'hasnt': 4829, 'replied': 9449, 'hoe': 5048, 'recording': 9335, 'xiu': 12952, 'nk': 7786, 'gi': 4350, 'uss': 12274, 'awwwwww': 763, 'elisse': 3335, 'ksoo': 6178, 'tat': 11374, 'rdbut': 9267, 'glen': 4415, 'yoult': 13103, 'cledera': 2079, 'rancho': 9219, 'imperial': 5363, 'de': 2693, 'silang': 10295, 'subdivision': 11079, 'center': 1815, 'canampt': 1706, 'evry': 3560, 'cornwall': 2368, 'veritably': 12349, 'penny': 8437, 'addictedtoanalsex': 112, 'sweetbj': 11247, 'blowjob': 1298, 'mhhh': 7155, 'sed': 9959, 'seungh': 10062, 'mee': 7047, 'envious': 3457, 'lovey': 6670, 'dovey': 3103, 'workin': 12860, 'schade': 9884, 'isco': 5583, 'penis': 8436, 'hushed': 5242, 'nationpraying': 7601, 'louisianalafayette': 6640, 'matteroftheheart': 6979, 'waduhpantes': 12479, 'suspended': 11218, 'smoking': 10595, 'cliche': 2083, 'youuuu': 13127, 'rma': 9601, 'jersey': 5751, 'texted': 11497, 'jaclintiler': 5663, 'likeforlike': 6444, 'hotfmnoaidilforariana': 5140, 'pleasefrown': 8680, 'fuckkk': 4177, 'sanum': 9820, 'poutingllaollao': 8837, 'dessert': 2837, 'foood': 4027, 'glares': 4408, 'vines': 12406, 'figuring': 3868, 'choreo': 1992, 'offensive': 7967, 'yousad': 13115, 'yeyy': 13062, 'hd': 4867, 'sux': 11222, 'nothaveld': 7860, 'mosquitoe': 7376, 'bites': 1229, 'kinky': 6095, 'hsould': 5179, 'justget': 5897, 'shuffle': 10257, 'buckling': 1548, 'springs': 10857, 'millz': 7200, 'askies': 633, 'millzxytongue': 7201, 'awusasho': 756, 'mbeki': 7002, 'unlucky': 12190, 'briefly': 1474, 'eggs': 3292, 'spotted': 10847, 'greatful': 4554, 'brooke': 1507, 'cracked': 2441, 'maverickgamerjuly': 6986, 'external': 3627, 'sd': 9935, 'airdroid': 236, 'supported': 11182, 'cramps': 2448, 'unstan': 12204, 'tay': 11390, 'ngeze': 7711, 'parties': 8322, 'cocktaily': 2132, 'loudlycryingfaceloudlycryingfaceloudlycryingface': 6632, 'ams': 393, 'crashing': 2455, 'airplane': 242, 'poutingumbrellawithraindrops': 8868, 'pens': 8439, 'spares': 10781, 'guests': 4639, 'barcelona': 908, 'bilbao': 1180, 'sharyl': 10135, 'shane': 10117, 'deactivate': 2694, 'giddy': 4356, 'zipper': 13191, 'repair': 9437, 'offering': 7971, 'cont': 2297, 'englandfrown': 3422, 'wore': 12852, 'tempted': 11467, 'oreos': 8131, 'networks': 7680, 'ain': 231, 'lolipop': 6565, 'kebab': 5984, 'klappertart': 6125, 'moodboster': 7350, 'unprepared': 12200, 'sry': 10872, 'dresscode': 3137, 'caitlintierney': 1660, 'stab': 10875, 'meh': 7061, 'wrocilam': 12914, 'loowwfrown': 6603, 'mariebear': 6907, 'recovered': 9339, 'wayne': 12578, 'lossstolen': 6619, 'accidentally': 62, 'damaged': 2608, 'devices': 2857, 'warranty': 12545, 'repairs': 9438, 'facewithtearsofjoyfacewithtearsofjoyfacewithtearsofjoyboym': 3678, 'okhandfruits': 8007, 'kyddque': 6197, 'lmfaooso': 6538, 'fra': 4078, 'otamendi': 8159, 'ny': 7927, 'oncomingtaxistatueoflibertynightwithstars': 8055, 'stealth': 10942, 'bastard': 942, 'therapy': 11569, 'exhausting': 3586, 'understandable': 12144, 'switzerland': 11267, 'wolrd': 12819, 'isi': 5590, 'fyn': 4219, 'genuinely': 4319, 'nightmares': 7755, 'audio': 690, 'noooooooooooooooooo': 7834, 'deck': 2727, 'chairs': 1844, 'yk': 13074, 'resy': 9502, 'strms': 11034, 'memory': 7093, 'bruh': 1526, 'locked': 6550, 'fbc': 3794, 'mork': 7363, 'hotspotwithdanris': 5145, 'choclbear': 1975, 'sone': 10700, 'produce': 9003, 'potagermy': 8798, 'blight': 1274, 'mych': 7518, 'shiiit': 10174, 'prompts': 9034, 'claimed': 2051, 'aready': 573, 'soulmate': 10752, 'smilingfacefacewithtearsofjoygrinningfacewithsmilingeyes': 10573, 'hws': 5250, 'jouch': 5835, 'por': 8766, 'que': 9152, 'liceooooo': 6419, 'ayala': 768, 'tunnel': 12008, 'promised': 9023, 'centres': 1819, 'thatscold': 11543, 'lourdes': 6643, 'bangfrown': 891, 'pv': 9130, 'anywhere': 502, 'showbox': 10242, 'companion': 2213, 'skinny': 10371, 'zzzz': 13208, 'dubai': 3175, 'laribuggy': 6273, 'nutella': 7917, 'loses': 6616, 'couldve': 2393, 'sirius': 10330, 'goat': 4437, 'naut': 7606, 'frudging': 4155, 'numbers': 7912, 'bbz': 979, 'angeke': 424, 'sbali': 9868, 'khang': 6027, 'euuuwwww': 3527, 'worker': 12855, 'reach': 9273, 'styles': 11072, 'poutingnelle': 8844, 'jaysus': 5717, 'insecurities': 5479, 'buti': 1623, 'poop': 8749, 'rome': 9653, 'throat': 11649, 'eshaaax': 3493, 'llama': 6528, 'getwellsoonamber': 4338, 'ajol': 259, 'heath': 4908, 'ledger': 6348, 'permission': 8469, 'mugabi': 7457, 'leadssupersport': 6331, 'milkshake': 7194, 'witcher': 12791, 'papertown': 8296, 'bale': 859, 'bahay': 840, 'bahayan': 841, 'magisa': 6803, 'lang': 6256, 'sadlyf': 9755, 'bunso': 1594, 'sleeeeep': 10392, 'astonvilla': 658, 'berigaud': 1107, 'bakar': 851, 'louisianafrown': 6639, 'streams': 11020, 'allergic': 308, 'depressing': 2811, 'blaines': 1251, 'acoustic': 87, 'hernia': 4963, 'doctored': 3025, 'toxin': 11866, 'ariel': 584, 'slapped': 10388, 'slammed': 10386, 'bees': 1038, 'finddjderek': 3891, 'uuughhhh': 12286, 'grabe': 4516, 'smi': 10443, 'poutingjust': 8835, 'nemesis': 7663, 'xdconfusion': 12945, 'appealing': 529, 'neeeeeeeeeeeeeeeeein': 7646, 'saaaad': 9737, 'hanging': 4776, 'creases': 2465, 'homed': 5071, 'tanned': 11358, 'dallas': 2604, 'nichols': 7736, 'walks': 12517, 'infront': 5452, 'beato': 1007, 'tim': 11699, 'minha': 7217, 'deleicious': 2763, 'skinner': 10370, 'bliss': 1276, 'pcb': 8393, 'peregrines': 8455, 'tram': 11895, 'hav': 4845, 'apt': 556, 'worki': 12859, 'bldg': 1261, 'mmmmm': 7281, 'nicki': 7738, 'fucjikg': 4169, 'buynotanapologyonitunes': 1637, 'avalible': 717, 'frustrated': 4159, 'nw': 7924, 'sch': 9883, 'jeslyn': 5752, 'rooting': 9661, 'kuch': 6181, 'hua': 5184, 'bitching': 1226, 'newbies': 7686, 'miracle': 7236, 'wings': 12771, 'fail': 3700, 'growingheartgrowingheartgrowingheart': 4602, 'nux': 7920, 'hinanap': 5012, 'uy': 12291, 'sched': 9885, 'anyare': 486, 'entertain': 3442, 'typa': 12069, 'buddies': 1552, 'transparency': 11907, 'photoshop': 8543, 'decided': 2723, 'planner': 8647, 'helpppp': 4949, 'wearig': 12591, 'dri': 3142, 'prey': 8954, 'drained': 3120, 'ausfailia': 702, 'snow': 10628, 'footy': 4032, 'countsnd': 2405, 'row': 9684, 'mampms': 6856, 'kitkat': 6112, 'steeez': 10945, 'wearyfacewearyfacewearyfacecryingface': 12596, 'suger': 11116, 'olivia': 8027, 'bamps': 873, 'audition': 691, 'injurys': 5466, 'appendix': 536, 'appendicitis': 535, 'fack': 3686, 'nhl': 7718, 'hays': 4860, 'khamis': 6025, 'kadhafi': 5908, 'reaaly': 9270, 'naomi': 7576, 'arias': 583, 'contemporary': 2306, 'messed': 7133, 'slacke': 10384, 'jahat': 5672, 'discount': 2958, 'thorpe': 11628, 'payneourdaddy': 8382, 'guessed': 4637, 'nicely': 7732, 'esnho': 3496, 'node': 7797, 'directx': 2937, 'shading': 10098, 'uploaded': 12227, 'blackberry': 1243, 'povertyyouareevil': 8879, 'emm': 3371, 'lied': 6422, 'poutingyou': 8877, 'elgin': 3331, 'vava': 12323, 'makati': 6826, 'yellowheart': 13037, 'baon': 901, 'soak': 10634, 'bread': 1451, 'mush': 7483, 'theyd': 11586, 'ouat': 8172, 'blinkin': 1275, 'headack': 4872, 'tension': 11474, 'eritation': 3481, 'perspective': 8480, 'poutinglouis': 8838, 'endlessly': 3409, 'batting': 954, 'kiddo': 6045, 'rumbelle': 9712, 'irresponsibly': 5578, 'pakighinabi': 8257, 'pinkfinite': 8598, 'beb': 1022, 'migraine': 7179, 'coyote': 2431, 'unamusedfaceunamusedface': 12130, 'headache': 4870, 'baechu': 830, 'calibraskaep': 1672, 'elgato': 3330, 'ant': 473, 'unexpect': 12152, 'poutingbestfriend': 8815, 'tri': 11930, 'fainted': 3705, 'bp': 1416, 'subway': 11096, 'fragile': 4080, 'gap': 4259, 'plots': 8692, 'net': 7671, 'bungie': 1590, 'woohyun': 12843, 'guilty': 4643, 'davao': 2665, 'luckyyyy': 6707, 'eunhae': 3522, 'misplaced': 7242, 'dae': 2594, 'bap': 902, 'modoki': 7307, 'irwinstagram': 5580, 'plsfrown': 8694, 'huehue': 5190, 'rice': 9544, 'krispy': 6171, 'marshmallows': 6928, 'mmjunction': 7280, 'soulsurvivor': 10754, 'stafford': 10882, 'mixture': 7272, 'theyve': 11589, 'shems': 10160, 'lage': 6228, 'ramd': 9214, 'sorryfrown': 10737, 'munchkin': 7473, 'todayfrown': 11756, 'poutingbought': 8816, 'parting': 8323, 'giftso': 4363, 'juja': 5870, 'murugan': 7480, 'handles': 4767, 'bgtau': 1147, 'harap': 4801, 'bagi': 837, 'aminn': 383, 'fraand': 4079, 'grimacingface': 4578, 'bigbang': 1169, 'gg': 4342, 'sian': 10266, 'nicoleapage': 7743, 'hellish': 4936, 'thirstyyyy': 11605, 'chesties': 1942, 'nandos': 7574, 'bow': 1403, 'madexx': 6775, 'hen': 4953, 'rdd': 9268, 'dissipating': 2986, 'capeee': 1726, 'japan': 5695, 'outlive': 8186, 'xrays': 12960, 'dental': 2798, 'spinenothing': 10821, 'relief': 9408, 'popol': 8759, 'stomach': 10985, 'frog': 4146, 'genad': 4303, 'negotiable': 7651, 'huhuhuhuhu': 5207, 'bbmadeinmanila': 970, 'findavip': 3890, 'boyirl': 1413, 'yasss': 13000, 'june': 5889, 'itnot': 5628, 'laine': 6239, 'difficiency': 2905, 'customer': 2559, 'speed': 10806, 'rapists': 9234, 'crime': 2488, 'bachpan': 799, 'yaadein': 12982, 'finnair': 3907, 'heathrow': 4909, 'norwegian': 7848, 'batteries': 952, 'upvotes': 12240, 'keeno': 5989, 'whatthefuck': 12688, 'grotty': 4592, 'seeker': 9967, 'morality': 7360, 'fern': 3836, 'mimi': 7204, 'bali': 860, 'poutingshes': 8859, 'tooo': 11817, 'todayyesterday': 11761, 'pleaseeeee': 8678, 'lowbat': 6676, 'poutingotwolgrandtrailer': 8850, 'funkfrown': 4199, 'wewanticecream': 12666, 'sweat': 11236, 'eugh': 3521, 'sara': 9824, 'occasionally': 7949, 'izzys': 5652, 'dorm': 3082, 'choppy': 1990, 'contacted': 2299, 'infinites': 5434, 'hion': 5018, 'cayton': 1797, 'darcey': 2637, 'connor': 2280, 'spoke': 10835, 'ripped': 9573, 'bottle': 1395, 'roomits': 9657, 'roommateexperience': 9659, 'ic': 5271, 'te': 11410, 'autofollowback': 711, 'asianbut': 627, 'penguin': 8435, 'poutingsad': 8857, 'ljp': 6527, 'nowdays': 7877, 'pleass': 8683, 'rawrx': 9257, 'numb': 7909, 'dentist': 2799, 'ag': 181, 'misss': 7253, 'rid': 9550, 'tab': 11292, 'ucas': 12085, 'bigtime': 1174, 'rumor': 9714, 'poutingzayniscomingbackonjuly': 8878, 'warmest': 12541, 'greeting': 4564, 'chin': 1965, 'tickle': 11684, 'zikra': 13190, 'lusi': 6730, 'hasyaconfusion': 4831, 'nuggets': 7906, 'soms': 10695, 'browning': 1519, 'millies': 7198, 'ano': 459, 'stans': 10901, 'camping': 1699, 'mags': 6807, 'hateeeee': 4835, 'lease': 6339, 'written': 12913, 'sizing': 10353, 'gs': 4613, 'unsuccessful': 12205, 'earlobes': 3234, 'sue': 11107, 'dreary': 3134, 'noooooo': 7830, 'denise': 2796, 'murielle': 7478, 'ahours': 221, 'prbrand': 8901, 'lyra': 6751, 'kathd': 5965, 'responded': 9487, 'chopped': 1989, 'wbu': 12582, 'activity': 94, 'kme': 6130, 'cram': 2447, 'curious': 2543, 'announcements': 453, 'trespassers': 11928, 'clandestins': 2053, 'loudlycryingfacesparklingheart': 6636, 'muller': 7464, 'obvious': 7944, 'mufc': 7455, 'stu': 11050, 'buddyyyy': 1554, 'feelgoodfriday': 3812, 'camp': 1696, 'forest': 4039, 'babysit': 793, 'stayed': 10937, 'opixer': 8097, 'pilllow': 8586, 'fool': 4026, 'bragging': 1424, 'skrillah': 10376, 'drowned': 3163, 'gue': 4635, 'muchha': 7446, 'grans': 4537, 'sjkaos': 10356, 'angrynow': 436, 'disappointedmmsrry': 2950, 'stuffy': 11063, 'boconfusion': 1323, 'honma': 5090, 'yeh': 13027, 'walay': 12509, 'bohat': 1329, 'wailay': 12492, 'preseason': 8929, 'shor': 10216, 'machaya': 6765, 'samjha': 9797, 'shopaholic': 10213, 'smilingfacewithopenmouthsmilingeyesthumbsup': 10581, 'pensivefacefacewithcoldsweat': 8442, 'sirkay': 10331, 'wali': 12511, 'pyaaz': 9135, 'daal': 2587, 'onions': 8061, 'vinegar': 12404, 'cooking': 2344, 'soho': 10666, 'wobbly': 12812, 'ciao': 2025, 'masaan': 6939, 'muv': 7507, 'beast': 1001, 'hayst': 4861, 'hnnnnnnn': 5044, 'saaad': 9738, 'rsa': 9692, 'ates': 662, 'whileconfusion': 12704, 'optimisation': 8111, 'soniii': 10706, 'kahaaaa': 5912, 'freezing': 4099, 'fml': 3987, 'jackets': 5659, 'sleepy': 10403, 'bullying': 1580, 'racial': 9184, 'halls': 4750, 'looooool': 6598, 'slutcake': 10429, 'playfrown': 8664, 'sobs': 10640, 'busier': 1617, 'onwards': 8071, 'coincidence': 2144, 'imac': 5341, 'grams': 4526, 'lived': 6511, 'nearer': 7631, 'blainedarren': 1250, 'fuuuuuuuuuck': 4212, 'gishwhes': 4392, 'excluded': 3576, 'angelica': 426, 'pulled': 9090, 'poutinggot': 8824, 'movement': 7399, 'frou': 4151, 'vaccines': 12296, 'armor': 589, 'legendary': 6360, 'cash': 1773, 'effort': 3289, 'trips': 11947, 'nat': 7590, 'brake': 1434, 'grumpy': 4610, 'hahahax': 4711, 'wrecking': 12905, 'gahhh': 4225, 'terible': 11482, 'kiligs': 6063, 'sugg': 11117, 'weeksurprise': 12627, 'blacktomorrow': 1248, 'weaker': 12585, 'shravan': 10253, 'maas': 6758, 'stooooop': 10991, 'giguilty': 4369, 'akooooooooooooooo': 268, 'imveryverysorry': 5377, 'poutingso': 8861, 'greyed': 4572, 'basename': 932, 'theme': 11556, 'sessions': 10051, 'cigar': 2028, 'speakers': 10792, 'anyways': 500, 'offered': 7970, 'promethazine': 9021, 'zopiclone': 13201, 'addition': 117, 'quetiapine': 9161, 'modified': 7305, 'releasetypically': 9404, 'prescription': 8928, 'greska': 4569, 'macedonian': 6764, 'slovak': 10420, 'hiking': 4999, 'tested': 11490, 'browsers': 1524, 'zokay': 13195, 'accentconfusion': 56, 'bbut': 975, 'gintama': 4379, 'shinsengumi': 10177, 'chapters': 1877, 'crapple': 2452, 'agrees': 206, 'phandroid': 8508, 'tline': 11740, 'orchestra': 8124, 'rehearsal': 9380, 'bittersweetness': 1236, 'eunji': 3523, 'bakit': 853, 'ehdar': 3296, 'pegea': 8427, 'panga': 8285, 'dosto': 3091, 'poutingopen': 8849, 'realliampayne': 9297, 'dmed': 3014, 'canceled': 1710, 'alreaddyyy': 334, 'luceleva': 6699, 'naeun': 7538, 'kidneys': 6047, 'ink': 5468, 'themselvesihatesomepeople': 11559, 'table': 11293, 'hardwired': 4808, 'canadian': 1704, 'acne': 84, 'poutinggulo': 8826, 'poutingkandekjs': 8836, 'rize': 9593, 'meydanconfusion': 7149, 'experienced': 3605, 'fcking': 3798, 'crei': 2477, 'connection': 2277, 'dormmates': 3083, 'bo': 1314, 'activation': 92, 'cod': 2137, 'redeem': 9347, 'invalid': 5545, 'wag': 12482, 'hopia': 5108, 'editor': 3270, 'revealed': 9522, 'boooo': 1366, 'extensions': 3626, 'rightnow': 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2869, 'poutingguess': 8825, 'jam': 5681, 'cheering': 1921, 'cornettos': 2366, 'nosebleed': 7850, 'nintendo': 7771, 'wew': 12665, 'ramos': 9216, 'ground': 4593, 'shawn': 10142, 'mendes': 7097, 'xconfusion': 12942, 'dinghy': 2924, 'skye': 10380, 'namedescription': 7565, 'gagal': 4223, 'txt': 12060, 'sims': 10309, 'noooot': 7838, 'notch': 7854, 'thts': 11661, 'starving': 10921, 'pyjamas': 9136, 'envy': 3459, 'suks': 11130, 'swifties': 11257, 'sorna': 10733, 'db': 2684, 'lurgy': 6727, 'kits': 6114, 'scaring': 9877, 'fenestoscope': 3831, 'etienne': 3516, 'bandana': 881, 'dressfrown': 3139, 'valeria': 12304, 'iyer': 5647, 'suriya': 11199, 'dangling': 2630, 'mjhe': 7275, 'aaj': 12, 'kisi': 6101, 'eyesight': 3645, 'everyonegood': 3547, 'aftenoon': 170, 'booooore': 1367, 'uuu': 12285, 'boyfriend': 1412, 'concerts': 2242, 'garage': 4260, 'denim': 2793, 'ce': 1803, 'gws': 4674, 'greys': 4574, 'anatomy': 406, 'morisettes': 7362, 'nontrial': 7821, 'sayhername': 9863, 'lootcrate': 6602, 'inca': 5389, 'trail': 11886, 'sandboarding': 9807, 'cancels': 1713, 'derby': 2815, 'mugs': 7458, 'unable': 12125, 'lavigne': 6307, 'signature': 10289, 'dishes': 2973, 'unfamiliar': 12155, 'mediadesk': 7040, 'coldest': 2150, 'thirdwheeling': 11602, 'lovebirds': 6648, 'imo': 5358, 'babytalk': 795, 'isfamiliar': 5585, 'ahhhhh': 216, 'stages': 10884, 'realising': 9289, 'copies': 2354, 'sensiesha': 10016, 'eldest': 3321, 'worriedface': 12879, 'keedz': 5987, 'pleaseif': 8681, 'thoits': 11620, 'taybigail': 11391, 'jordans': 5828, 'tournaments': 11857, 'kinks': 6094, 'chargers': 1887, 'streak': 11016, 'scorch': 9904, 'srsky': 10869, 'tdc': 11406, 'insensitivenessridiculousfrown': 5481, 'cooperating': 2351, 'conversion': 2333, 'thurston': 11672, 'collins': 2168, 'quietly': 9169, 'kennel': 6001, 'rnfrown': 9604, 'pluckerssss': 8700, 'gion': 4380, 'kidschoiceawards': 6049, 'recorded': 9334, 'ming': 7215, 'pbr': 8391, 'periscope': 8468, 'uts': 12282, 'shawty': 10143, 'naw': 7609, 'sterlings': 10960, 'muses': 7482, 'hrryok': 5173, 'wnt': 12807, 'bueno': 1559, 'receptionist': 9319, 'minsyou': 7229, 'ella': 3338, 'ketchup': 6010, 'tasteless': 11371, 'deantd': 2702, 'justgotkanekified': 5898, 'notgonnabeactiveforweeksdontmissittoomuch': 7859, 'vlog': 12435, 'chills': 1963, 'turtles': 12017, 'cnn': 2119, 'strapline': 11013, 'theatres': 11546, 'guncontrol': 4653, 'lafayette': 6226, 'stung': 11065, 'poutinga': 8812, 'powerpoint': 8884, 'expectations': 3597, 'diner': 2922, 'nono': 7818, 'hinde': 5013, 'jc': 5722, 'circuit': 2039, 'secondary': 9951, 'joins': 5808, 'sodders': 10655, 'mobitel': 7294, 'playstation': 8670, 'charged': 1885, 'exp': 3594, 'misspelt': 7252, 'hyungwon': 5263, 'alarms': 277, 'cancelling': 1712, 'needicecreamnow': 7643, 'shakes': 10107, 'repeatedly': 9443, 'nuuh': 7919, 'jace': 5657, 'mostest': 7377, 'ar': 560, 'supercard': 11168, 'ridacongratulation': 9552, 'vips': 12414, 'babefrown': 785, 'sadbad': 9748, 'urgh': 12247, 'grigsons': 4575, 'carrot': 1764, 'ughh': 12094, 'enable': 3396, 'otter': 8167, 'protection': 9049, 'argh': 576, 'pon': 8740, 'otl': 8164, 'sleepovers': 10400, 'jesse': 5754, 'fabina': 3655, 'meantfacewithtearsofjoyfacewithtearsofjoy': 7031, 'gardening': 4263, 'barristas': 924, 'pup': 9104, 'drivers': 3152, 'brolly': 1505, 'buckner': 1549, 'dey': 2863, 'serve': 10041, 'bitin': 1230, 'pretzels': 8948, 'bblf': 967, 'vanilla': 12315, 'latte': 6296, 'poa': 8713, 'rog': 9637, 'skulker': 10377, 'threatongue': 11643, 'pgq': 8505, 'hungrrrryyy': 5228, 'icloud': 5279, 'ipod': 5564, 'hallyu': 4751, 'mac': 6759, 'buuuut': 1632, 'ber': 1104, 'okie': 8008, 'harlo': 4809, 'torrentialrain': 11838, 'aconfusion': 85, 'lloyds': 6529, 'knowwww': 6149, 'runny': 9720, 'sweater': 11237, 'ford': 4035, 'rightand': 9561, 'intolerant': 5539, 'xenophobes': 12946, 'wtfff': 12924, 'newer': 7690, 'pish': 8611, 'comparison': 2218, 'remastered': 9415, 'fe': 3799, 'icons': 5281, 'barker': 917, 'appear': 530, 'cornetto': 2364, 'strawberry': 11015, 'cornettoconfusion': 2365, 'nowwhen': 7886, 'kapatidkongpogi': 5935, 'mel': 7074, 'longest': 6588, 'carmen': 1754, 'login': 6558, 'wingstop': 12772, 'loool': 6596, 'budge': 1555, 'fuq': 4202, 'ilhoon': 5326, 'getthescoop': 4334, 'hearess': 4889, 'txtshot': 12061, 'unfollowing': 12161, 'standby': 10895, 'inatall': 5386, 'zenmate': 13186, 'carefulnamechecking': 1745, 'whistle': 12710, 'junmyeon': 5894, 'dddy': 2691, 'ketepi': 6011, 'arini': 585, 'je': 5726, 'igbo': 5307, 'blamehoney': 1254, 'whhr': 12703, 'trilla': 11940, 'samhernandez': 9796, 'snuggle': 10633, 'usage': 12259, 'warning': 12544, 'animator': 441, 'poutingcrying': 8819, 'vertigo': 12356, 'panic': 8286, 'attacks': 679, 'dual': 3173, 'carriageway': 1762, 'poutingaragalang': 8813, 'tams': 11351, 'boses': 1386, 'theo': 11564, 'anymoreeeeee': 489, 'cactus': 1652, 'sorrry': 10735, 'bowel': 1404, 'disease': 2966, 'tumour': 12002, 'waited': 12495, 'puffy': 9085, 'eyelids': 3642, 'musicas': 7488, 'campsites': 1701, 'sooooo': 10718, 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1720, 'khloes': 6032, 'nhe': 7716, 'yar': 12996, 'minkyuk': 7225, 'hols': 5066, 'isolated': 5600, 'sensor': 10017, 'broker': 1504, 'wna': 12805, 'flaviana': 3944, 'chickmt': 1951, 'letsfootball': 6393, 'atk': 667, 'greymind': 4573, 'gayle': 4281, 'mooddump': 7351, 'livestream': 6518, 'sneaking': 10617, 'felton': 3827, 'verity': 12350, 'standens': 10896, 'sponge': 10837, 'smilingfacewithopenmouthclosedeyessmilingfacewithopenmouthclosedeyes': 10579, 'takoyaki': 11325, 'aisyah': 250, 'ffvi': 3851, 'youtubegpctsojkw': 13123, 'donutsss': 3073, 'pizzas': 8631, 'grated': 4543, 'sparse': 10788, 'lagifrown': 6230, 'hoodafc': 5095, 'yescant': 13048, 'riderfrown': 9555, 'hueeeee': 5189, 'passwordrecall': 8343, 'thingy': 11596, 'george': 4321, 'chewing': 1945, 'stella': 10951, 'yelled': 13033, 'theaccidentalcouple': 11544, 'smooth': 10597, 'handover': 4769, 'spick': 10814, 'offense': 7966, 'bebii': 1025, 'happenend': 4783, 'dr': 3112, 'poutingwhat': 8874, 'balms': 868, 'hmph': 5041, 'bubba': 1542, 'cuddles': 2528, 'cuddling': 2529, 'floor': 3971, 'oi': 7994, 'bengalis': 1096, 'masterchef': 6959, 'whatchya': 12680, 'petrol': 8498, 'diesel': 2898, 'cock': 2128, 'drown': 3162, 'nyquil': 7931, 'poooootek': 8748, 'leaking': 6333, 'thermos': 11579, 'casting': 1779, 'smilingfacewithsmilingeyessmilingfacewithsmilingeyessmilingfacewithsmilingeyesredheart': 10584, 'skate': 10359, 'twoheartstwoheartstwoheartstwohearts': 12054, 'tae': 11300, 'kita': 6109, 'crys': 2521, 'ia': 5265, 'nation': 7599, 'corrupted': 2373, 'anythingsurely': 496, 'october': 7956, 'ene': 3412, 'zehr': 13183, 'khany': 6028, 'grocery': 4589, 'hubba': 5187, 'bubble': 1543, 'gum': 4650, 'closet': 2110, 'jhalak': 5764, 'bakwas': 854, 'seehiah': 9964, 'omggg': 8046, 'goys': 4512, 'nachos': 7533, 'braid': 1425, 'braids': 1426, 'boongfrown': 1365, 'recommendations': 9331, 'cwnt': 2573, 'trivia': 11949, 'crack': 2440, 'bdays': 988, 'ownconfusion': 8219, 'rohingya': 9641, 'muslims': 7493, 'indicted': 5415, 'trafficking': 11883, 'charges': 1888, 'thailand': 11516, 'savetherohingya': 9855, 'sabaa': 9741, 'rumble': 9713, 'kumble': 6185, 'scold': 9900, 'cleaning': 2072, 'phrased': 8547, 'include': 5396, 'themi': 11558, 'melting': 7085, 'tfw': 11502, 'jestconfusion': 5756, 'relaxes': 9398, 'offended': 7964, 'sleepingwithsirens': 10398, 'bringmethehorizon': 1487, 'dzul': 3227, 'carva': 1770, 'louisiana': 6638, 'regularly': 9377, 'sympathis': 11278, 'expected': 3598, 'revamps': 9521, 'mosquito': 7375, 'headphones': 4877, 'poutingnow': 8846, 'wacha': 12476, 'niende': 7745, 'kk': 6122, 'calibraksaep': 1670, 'darlin': 2646, 'grounded': 4594, 'doednt': 3029, 'meaningful': 7027, 'horrific': 5119, 'royally': 9687, 'sweedy': 11243, 'maui': 6983, 'pooo': 8745, 'nobodies': 7794, 'sacconejolys': 9744, 'bethesda': 1131, 'likee': 6442, 'kateee': 5961, 'iloveyouu': 5335, 'riixc': 9567, 'linux': 6481, 'meital': 7072, 'nawwwe': 7613, 'chikka': 1954, 'ug': 12091, 'rata': 9247, 'soonest': 10714, 'mwamwa': 7514, 'juarez': 5857, 'faggot': 3695, 'opener': 8089, 'bitsenpai': 1232, 'nicest': 7733, 'mehendi': 7067, 'dash': 2652, 'bookmark': 1357, 'whay': 12690, 'shaa': 10092, 'pramis': 8896, 'kissingfacewithclosedeyes': 6105, 'ngee': 7710, 'crikey': 2487, 'snit': 10622, 'nathanielhinanakit': 7596, 'naya': 7615, 'spinny': 10823, 'loading': 6541, 'wheel': 12693, 'poutingi': 8830, 'notifs': 7868, 'poutingin': 8832, 'dammitstop': 2611, 'guyslol': 4667, 'albeit': 280, 'disappointing': 2951, 'athlete': 665, 'stripes': 11033, 'gfriend': 4340, 'fugly': 4181, 'smilingfacewithsmilingeyesrevolvinghearts': 10583, 'jongdaei': 5823, 'tlists': 11741, 'budget': 1556, 'pabebegirls': 8230, 'pabebe': 8229, 'altered': 340, 'sandra': 9809, 'storify': 11003, 'londonfrown': 6576, 'mtvhottest': 7433, 'gaga': 4222, 'cage': 1658, 'cryingfacedizzyfaceloudlycryingface': 2518, 'hulkamania': 5210, 'unloved': 12188, 'ihhh': 5314, 'stackare': 10879, 'brownxox': 1520, 'remedy': 9417, 'ov': 8196, 'raiz': 9204, 'nvr': 7923, 'gv': 4670, 'upwt': 12242, 'nowwt': 7887, 'achievedin': 79, 'thr': 11638, 'soln': 10672, 'whaaaaaaaaaat': 12671, 'pipes': 8603, 'airwolf': 248, 'lawn': 6311, 'pierce': 8574, 'cupids': 2542, 'torn': 11836, 'retainers': 9505, 'haiss': 4732, 'todayy': 11760, 'thoo': 11622, 'trainsec': 11894, 'sprees': 10854, 'everday': 3539, 'poutingit': 8834, 'william': 12751, 'umboh': 12118, 'addicts': 115, 'jadines': 5668, 'thiz': 11613, 'iz': 5648, 'emeged': 3364, 'kennat': 6000, 'sav': 9848, 'reunite': 9517, 'abi': 30, 'arctic': 569, 'chicsirific': 1953, 'struggle': 11046, 'structured': 11044, 'mootongue': 7359, 'cumbia': 2534, 'poutingwearyface': 8872, 'badlife': 824, 'nameisdan': 7567, 'kaslkdja': 5957, 'wks': 12802, 'feverfew': 3846, 'weddingflowers': 12611, 'diyflowers': 3003, 'insyaf': 5512, 'fitnes': 3922, 'wolverine': 12820, 'innocent': 5471, 'foldedhandslightskintonebirthdaycake': 3997, 'buyorder': 1638, 'mememe': 7090, 'krystoria': 6176, 'snob': 10623, 'poutingsakit': 8858, 'zumba': 13203, 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5724, 'jimins': 5775, 'fancafe': 3737, 'wtvd': 12927, 'waffle': 12481, 'himseek': 5010, 'glo': 4418, 'poutingpls': 8853, 'cory': 2375, 'monteith': 7340, 'louth': 6644, 'hashbrownsfacewithtearsofjoysmilingfacewithsmilingeyesheartwitharrowheartwitharrow': 4824, 'pgs': 8506, 'msc': 7417, 'hierro': 4984, 'shirleycam': 10185, 'pal': 8263, 'gilet': 4370, 'cheeks': 1918, 'squishy': 10864, 'lahhh': 6236, 'eon': 3460, 'sunrise': 11157, 'beety': 1040, 'getaway': 4332, 'committing': 2204, 'criminal': 2489, 'amiibo': 380, 'habe': 4686, 'siannn': 10267, 'chuckin': 2016, 'ampsha': 392, 'nia': 7721, 'seniors': 10013, 'replacement': 9445, 'strap': 11012, 'dz': 3226, 'entlead': 3447, 'ifsc': 5304, 'mayor': 6996, 'biodiversity': 1195, 'taxonomic': 11389, 'infrastructure': 5450, 'collaboration': 2154, 'species': 10799, 'suppl': 11179, 'pdf': 8400, 'collar': 2158, 'belting': 1082, 'smith': 10591, 'rides': 9557, 'eyeliner': 3643, 'therefore': 11572, 'netherlands': 7676, 'disgusted': 2969, 'el': 3315, 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'peacefrown': 8404, 'artists': 610, 'creamist': 2464, 'pleaseeeeefrown': 8679, 'challenge': 1848, 'substitution': 11093, 'cplt': 2435, 'cp': 2434, 'hannah': 4778, 'blueheart': 1301, 'opus': 8116, 'bbq': 974, 'lolliver': 6567, 'split': 10829, 'collat': 2159, 'spilt': 10819, 'poutingquitkarwaoyaaro': 8855, 'sherep': 10163, 'nemen': 7662, 'behey': 1056, 'motherfucker': 7382, 'mads': 6786, 'reece': 9358, 'vm': 12437, 'deth': 2848, 'ovi': 8215, 'lest': 6389, 'departure': 2805, 'madhvitommo': 6777, 'ammilliam': 386, 'wipe': 12780, 'yuck': 13139, 'ystrday': 13134, 'seolhyun': 10021, 'spicy': 10815, 'owls': 8218, 'latersign': 6292, 'suunn': 11220, 'wallpaper': 12522, 'cba': 1799, 'hilife': 5003, 'rec': 9310, 'gotdamn': 4504, 'baaack': 779, 'srw': 10871, 'mobage': 7290, 'passes': 8338, 'digitals': 2913, 'sexcams': 10072, 'interruptd': 5536, 'strokes': 11036, 'gnr': 4431, 'partied': 8321, 'backstage': 813, 'slash': 10389, 'tagged': 11307, 'bunny': 1591, 'sooner': 10713, 'kwon': 6196, 'financial': 3887, 'analyst': 403, 'expedia': 3600, 'bellevue': 1076, 'waconfusion': 12478, 'finance': 3886, 'expediajobs': 3601, 'prison': 8977, 'dose': 3087, 'huhuh': 5204, 'heartburn': 4896, 'listens': 6497, 'againawalmu': 184, 'njareeeeem': 7782, 'nooooooooo': 7833, 'psycho': 9065, 'wahhh': 12488, 'abudhabi': 48, 'blues': 1303, 'hibye': 4978, 'shareyoursummer': 10127, 'mustbe': 7498, 'dairy': 2601, 'produxt': 9008, 'lactose': 6217, 'midlands': 7174, 'roblong': 9621, 'knackered': 6132, 'footage': 4029, 'icing': 5278, 'dragged': 3114, 'lifeless': 6428, 'knowwwww': 6150, 'pricing': 8960, 'lakeland': 6242, 'hoursembarrassed': 5153, 'pengen': 8434, 'boxing': 1409, 'indonesiaconfusion': 5427, 'girlll': 4387, 'tsunami': 11981, 'indies': 5419, 'wnick': 12806, 'picks': 8561, 'tirade': 11720, 'stoop': 10992, 'lower': 6677, 'thunder': 11667, 'fight': 3861, 'apologize': 521, 'adults': 141, 'facade': 3657, 'democracy': 2789, 'hye': 5253, 'brat': 1443, 'bade': 822, 'fursat': 4206, 'usey': 12271, 'banaya': 878, 'uppar': 12231, 'waale': 12475, 'ney': 7704, 'afsos': 169, 'humse': 5219, 'dur': 3207, 'gif': 4358, 'whod': 12718, 'naruhina': 7584, 'nameeee': 7566, 'haiqal': 4725, 'picc': 8557, 'instore': 5506, 'prevoting': 8952, 'durian': 3209, 'strudel': 11045, 'tsk': 11979, 'marines': 6910, 'kailan': 5917, 'separated': 10024, 'payday': 8377, 'payhour': 8378, 'immediately': 5354, 'mekele': 7073, 'wrongfrown': 12916, 'guppy': 4658, 'poorkid': 8753, 'misunderstood': 7263, 'cuddly': 2530, 'scratches': 9917, 'tries': 11939, 'thumb': 11663, 'ankles': 445, 'torturing': 11840, 'caste': 1778, 'kirkiri': 6098, 'hearted': 4899, 'facewithtearsofjoyrevolvinghearts': 3682, 'wonho': 12836, 'mission': 7251, 'pap': 8291, 'danzell': 2636, 'crafting': 2445, 'devil': 2858, 'sheff': 10155, 'utdyork': 12280, 'visa': 12419, 'bench': 1088, 'catlin': 1790, 'facewithtearsofjoywearyfacewearyface': 3684, 'harmful': 4811, 'motivation': 7386, 'yolo': 13086, 'bloating': 1277, 'ollys': 8032, 'alternis': 345, 'overall': 8199, 'continental': 2313, 'grinningfacepistol': 4585, 'thirsty': 11604, 'bhatti': 1152, 'konami': 6161, 'pes': 8485, 'rantie': 9232, 'perverse': 8483, 'bracelets': 1421, 'twins': 12043, 'bylfnnz': 1645, 'banned': 896, 'pakistan': 8258, 'failsatlife': 3704, 'poutingwaaah': 8870, 'jaebum': 5669, 'ahmad': 218, 'maslan': 6949, 'cooks': 2345, 'hull': 5212, 'supporter': 11183, 'misserable': 7247}


                        # Model Generation Using Multinomial Naive Bayes
clf = MultinomialNB().fit(X_train, y_train)
predicted = clf.predict(X_test)

print("MultinomialNB Accuracy:", metrics.accuracy_score(y_test, predicted))

MultinomialNB Accuracy: 0.973

	tweets
0	#FollowFriday @France_Inte @PKuchly57 @Milipol...
1	@Lamb2ja Hey James! How odd :/ Please call our...
2	@DespiteOfficial we had a listen last night :)...
3	@97sides CONGRATS :)
4	yeaaaah yippppy!!! my accnt verified rqst has...
...	...
4995	@chriswiggin3 Chris, that's great to hear :) D...
4996	@RachelLiskeard Thanks for the shout-out :) It...
4997	@side556 Hey! :) Long time no talk...
4998	@staybubbly69 as Matt would say. WELCOME TO AD...
4999	@DanielOConnel18 you could say he will have eg...

	tweets	sentiment	find_url
0	#followfriday @france_inte @pkuchly57 @milipol...	1	-1
1	@lamb2ja hey james! how odd :/ please call our...	1	-1
2	@despiteofficial we had a listen last night :)...	1	-1
3	@97sides congrats :)	1	-1
4	yeaaaah yippppy!!! my accnt verified rqst has...	1	-1
5	@bhaktisbanter @pallaviruhail this one is irre...	1	81
6	we don't like to keep our lovely customers wai...	1	104
7	@impatientraider on second thought, there’s ju...	1	-1
8	jgh , but we have to go to bayan :d bye	1	-1
9	as an act of mischievousness, am calling the e...	1	-1

	tweets	sentiment	clean_tweets
0	#followfriday @france_inte @pkuchly57 @milipol...	1	#followfriday @france_inte @pkuchly57 @milipol...
1	@lamb2ja hey james! how odd :/ please call our...	1	@lamb2ja hey james! how odd Skeptical annoyed ...
2	@despiteofficial we had a listen last night :)...	1	@despiteofficial we had a listen last night Ha...
3	@97sides congrats :)	1	@97sides congrats Happy face or smiley
4	yeaaaah yippppy!!! my accnt verified rqst has...	1	yeaaaah yippppy!!! my accnt verified rqst has...
5	@bhaktisbanter @pallaviruhail this one is irre...	1	@bhaktisbanter @pallaviruhail this one is irre...
6	we don't like to keep our lovely customers wai...	1	we don't like to keep our lovely customers wai...
7	@impatientraider on second thought, there’s ju...	1	@impatientraider on second thought, there’s ju...
8	jgh , but we have to go to bayan :d bye	1	jgh , but we have to go to bayan :d bye
9	as an act of mischievousness, am calling the e...	1	as an act of mischievousness, am calling the e...

	tweets	sentiment	clean_tweets
0	#followfriday @france_inte @pkuchly57 @milipol...	1	followfriday inte paris for being top engaged...
1	@lamb2ja hey james! how odd :/ please call our...	1	hey james how odd Skeptical annoyed undecided...
2	@despiteofficial we had a listen last night :)...	1	we had a listen last night Happy face or smil...
3	@97sides congrats :)	1	congrats Happy face or smiley
4	yeaaaah yippppy!!! my accnt verified rqst has...	1	yeaaaah yippppy my accnt verified rqst has su...

	tweets	sentiment	tweets_token
0	followfriday inte paris top engaged members co...	1	[followfriday, inte, paris, top, engaged, memb...
1	hey james odd skeptical annoyed undecided unea...	1	[hey, james, odd, skeptical, annoyed, undecide...
2	listen last night happy face smiley bleed amaz...	1	[listen, last, night, happy, face, smiley, ble...
3	congrats happy face smiley	1	[congrats, happy, face, smiley]
4	yeaaaah yippppy accnt verified rqst succeed go...	1	[yeaaaah, yippppy, accnt, verified, rqst, succ...
...	...	...	...
9995	wan na change avi usanele frown sad andry pouting	0	[wan, na, change, avi, usanele, frown, sad, an...
9996	puppy broke foot frown sad andry pouting	0	[puppy, broke, foot, frown, sad, andry, pouting]
9997	wheres jaebum baby pictures frown sad andry po...	0	[wheres, jaebum, baby, pictures, frown, sad, a...
9998	mr ahmad maslan cooks frown sad andry pouting	0	[mr, ahmad, maslan, cooks, frown, sad, andry, ...
9999	hull supporter expecting misserable weeks frow...	0	[hull, supporter, expecting, misserable, weeks...

Natural Language Processing (NLP)¶

What is NLP ?¶

A little bit of the NLP history¶

NLP Applications¶

NLP methods, techniques and key concepts:¶

Which libraries can we use?¶

Getting the data we're going to use ready.¶

Adding the tweets to new DataFrames using Pandas¶

Preprocessing¶

What is text processing?¶

Preprocessing Tasks¶

Cleaning text ¶

Converting any uppercase string to lowercase.¶

Removing URL's using the `find()` method from the String :¶

Replacing emojis and emoticons with words¶

Stopwords¶

Selecting stop words¶

Tokenization¶

Using Collections library¶

Stemming/Lemmatization¶

- Stemming¶

- Lemmatization¶

Generating tags for each token in the text to Normalize sentences and to lemmatize each word using the tag¶

Tagging using `pos_tag`¶

Bag of words model¶

Can we improve the bag of words model? ¶

TF-IDF (term frequency–inverse document frequency)¶

	tweets
0	hopeless for tmr :(
1	Everything in the kids section of IKEA is so c...
2	@Hegelbon That heart sliding into the waste ba...
3	“@ketchBurning: I hate Japanese call him "bani...
4	Dang starting next week I have "work" :(
...	...
4995	I wanna change my avi but uSanele :(
4996	MY PUPPY BROKE HER FOOT :(
4997	where's all the jaebum baby pictures :((
4998	But but Mr Ahmad Maslan cooks too :( https://t...
4999	@eawoman As a Hull supporter I am expecting a ...

	tweets	sentiment	sent_descr
0	#FollowFriday @France_Inte @PKuchly57 @Milipol...	1	positive
1	@Lamb2ja Hey James! How odd :/ Please call our...	1	positive
2	@DespiteOfficial we had a listen last night :)...	1	positive
3	@97sides CONGRATS :)	1	positive
4	yeaaaah yippppy!!! my accnt verified rqst has...	1	positive

	tweets	sent_descr
0	hopeless for tmr :(	negative
1	Everything in the kids section of IKEA is so c...	negative
2	@Hegelbon That heart sliding into the waste ba...	negative
3	“@ketchBurning: I hate Japanese call him "bani...	negative
4	Dang starting next week I have "work" :(	negative

	tweets	sentiment	find_url
5	@bhaktisbanter @pallaviruhail this one is irre...	1	81
6	we don't like to keep our lovely customers wai...	1	104
11	who wouldn't love these big....juicy....selfie...	1	53
12	@mish23615351 follow @jnlazts & http://t....	1	37
14	great new opportunity for junior triathletes a...	1	107
19	hello i need to know something can u fm me on ...	1	80
27	spiritual ritual festival (népal)\nbeginning o...	1	105
30	my kik - hatessuce32429 #kik #kikme #lgbt #tin...	1	74
35	@jamiefigsxx follow @jnlazts & http://t.c...	1	36
43	i added a video to a @youtube playlist http://...	1	39

	token	count
1520	sad	5272
3880	andry	5147
3881	pouting	5037
3879	frown	4981
8	happy	4498
...	...	...
2324	accumulated	1
7212	smileyplz	1
7211	tweeeps	1
7210	afterznoon	1
13227	misserable	1

	tweets	sentiment	tweets_token	t_stem
0	followfriday inte paris top engaged members co...	1	[followfriday, inte, paris, top, engaged, memb...	[followfriday, int, pari, top, engag, member, ...
1	hey james odd skeptical annoyed undecided unea...	1	[hey, james, odd, skeptical, annoyed, undecide...	[hey, jame, odd, skeptic, annoy, undecid, unea...
2	listen last night happy face smiley bleed amaz...	1	[listen, last, night, happy, face, smiley, ble...	[listen, last, night, happi, face, smiley, ble...
3	congrats happy face smiley	1	[congrats, happy, face, smiley]	[congrat, happi, face, smiley]
4	yeaaaah yippppy accnt verified rqst succeed go...	1	[yeaaaah, yippppy, accnt, verified, rqst, succ...	[yeaaaah, yippppi, accnt, verifi, rqst, succee...
...	...	...	...	...
9995	wan na change avi usanele frown sad andry pouting	0	[wan, na, change, avi, usanele, frown, sad, an...	[wan, na, chang, avi, usanel, frown, sad, andr...
9996	puppy broke foot frown sad andry pouting	0	[puppy, broke, foot, frown, sad, andry, pouting]	[puppi, broke, foot, frown, sad, andri, pout]
9997	wheres jaebum baby pictures frown sad andry po...	0	[wheres, jaebum, baby, pictures, frown, sad, a...	[where, jaebum, babi, pictur, frown, sad, andr...
9998	mr ahmad maslan cooks frown sad andry pouting	0	[mr, ahmad, maslan, cooks, frown, sad, andry, ...	[mr, ahmad, maslan, cook, frown, sad, andri, p...
9999	hull supporter expecting misserable weeks frow...	0	[hull, supporter, expecting, misserable, weeks...	[hull, support, expect, misser, week, frown, s...

	lemma	count
1475	sad	5272
3699	andry	5147
3700	pouting	5037
3698	frown	4981
8	happy	4498
...	...	...
6009	forklift	1
6008	todos	1
6006	bom	1
6005	nb	1
12387	misserable	1

	tag	count
1733	(sad, JJ)	5208
4610	(andry, NN)	5061
8	(happy, JJ)	4495
9	(face, NN)	4450
10	(smiley, NN)	3978
...	...	...
7844	(overdue, JJ)	1
7845	(mice, NN)	1
7846	(womandancingwomandancingwomandancingsa, NN)	1
7847	(joys, NNS)	1
16918	(misserable, JJ)	1

Natural Language Processing (NLP)¶

What is NLP ?¶

A little bit of the NLP history¶

NLP Applications¶

NLP methods, techniques and key concepts:¶

Which libraries can we use?¶

Getting the data we're going to use ready.¶

Adding the tweets to new DataFrames using Pandas¶

Preprocessing¶

What is text processing?¶

Preprocessing Tasks¶

Cleaning text ¶

Converting any uppercase string to lowercase.¶

Removing URL's using the find() method from the String :¶

Replacing emojis and emoticons with words¶

Stopwords¶

Selecting stop words¶

Tokenization¶

Using Collections library¶

Stemming/Lemmatization¶

- Stemming¶

- Lemmatization¶

Generating tags for each token in the text to Normalize sentences and to lemmatize each word using the tag¶

Tagging using pos_tag¶

Bag of words model¶

Can we improve the bag of words model? ¶

TF-IDF (term frequency–inverse document frequency)¶

Removing URL's using the `find()` method from the String :¶

Tagging using `pos_tag`¶