mostUsedWords.py

import re
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import string
import nltk
import warnings
from nltk.stem.porter import *
from wordcloud import WordCloud

warnings.filterwarnings("ignore", category=DeprecationWarning)
combi = pd.read_csv('ahmed.csv')

def remove_pattern(input_txt, pattern):
    r = re.findall(pattern, input_txt)
    for i in r:
        input_txt = re.sub(i, '', input_txt)

    return input_txt

combi['tidy_tweet'] = np.vectorize(remove_pattern)(combi['text'], "@[\w]*")
combi['tidy_tweet'] = combi['tidy_tweet'].str.replace("[^a-zA-Z#]", " ")
combi['tidy_tweet'] = combi['tidy_tweet'].apply(lambda x: ' '.join([w for w in x.split() if len(w)>3]))
tokenized_tweet = combi['tidy_tweet'].apply(lambda x: x.split())
stemmer = PorterStemmer()

tokenized_tweet = tokenized_tweet.apply(lambda x: [stemmer.stem(i) for i in x]) # stemming
tokenized_tweet.head()

for i in range(len(tokenized_tweet)):
    tokenized_tweet[i] = ' '.join(tokenized_tweet[i])

combi['tidy_tweet'] = tokenized_tweet

all_words = ' '.join([text for text in combi['tidy_tweet']])
wordcloud = WordCloud(width=800, height=500, random_state=21, max_font_size=110).generate(all_words)

plt.figure(figsize=(10, 7))
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')
plt.show()