Sentiment-Analysis/Sentiment Analysis at main · Aalian123/Sentiment-Analysis · GitHub

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import tweepy
from textblob import TextBlob
from  wordcloud import WordCloud
import pandas as pd
import numpy as np
import re
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')

 #Api credentials
  consumerkey=''
  consumerSecret=''
  accesstoken=''
  accesstokensecret=''


#Auth object
  authenticate=tweepy.OAuthHandler(consumerkey,consumerSecret)

#Access Token set
  authenticate.set_access_token(accesstoken,accesstokensecret)

#Api object while passing auth info
  api=tweepy.API(authenticate,wait_on_rate_limit=True)


#Extract tweets from the twitter user
  posts=api.user_timeline(screen_name="Trump", count=100, lang='en', tweet_mode='extended')

#Print tweets
  i=1
  for tweet in posts:
    print(str(i) + ')' + tweet.full_text + "\n")
    i=i+1


#create a data frame for tweets
  df=pd.DataFrame([tweet.full_text for tweet in posts],columns=['Tweets'])

#show first 5
  df.head()


#clean text
  def cleantxt(text):
    text=re.sub(r'@[A-Za-z0-9]+', '' ,text) # remove
    text=re.sub(r'#','',text) #Removing '#'
    text=re.sub(r'RT[\s]+','',text) # Removing RT
    text=re.sub(r'https:\/\/\s+','',text) # Removing hyperlink

    return text


#Applying
  df['Tweets']=df['Tweets'].apply(cleantxt)

#show clean text
  df


#get subjectivity
  def getsubj(text):
    return TextBlob(text).sentiment.subjectivity

#get polarity
  def getpol(text):
    return TextBlob(text).sentiment.polarity

#create new columns
  df['Subjectivity']=df['Tweets'].apply(getsubj)
  df['polarity']=df['Tweets'].apply(getpol)

#print
  df


#get positivity
  def positivity(score):
    if score<0:
      return 'Negative'
    elif score==0:
      return 'Neutral'
    else:
      return 'Positive'
  df['Analysis']=df['polarity'].apply(positivity)