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Commit 48526c4

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twitterSentimentalAnalysisScript (avinashkranjan#952)
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from nltk.sentiment import SentimentIntensityAnalyzer
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from textblob import TextBlob
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import tweepy
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class model(object):
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def __init__(self, candidate_key, candidate_sec, access_key, access_sec):
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super().__init__()
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self.candidate_key = candidate_key
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self.candidate_sec = candidate_sec
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self.access_key = access_key
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self.access_sec = access_sec
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def get_authenticated_api(self):
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auth = tweepy.OAuthHandler(self.candidate_key, self.candidate_sec)
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auth.set_access_token(self.access_key, self.access_sec)
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api = tweepy.API(auth)
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return api
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def get_live_tweets_from_Twitter(self, text):
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api = self.get_authenticated_api()
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tweet_live = api.search(text, tweet_mode='extended')
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return tweet_live
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def analysis_live_tweet_data(self, text):
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tweet_live = self.get_live_tweets_from_Twitter(text)
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for tweet in tweet_live:
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tweet_is = tweet.text
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analysis = TextBlob(tweet_is)
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def detailed_analysis_tweet_data(self, text):
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# if polarity is in negative then the tweet is negative
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# if in positive then its a positive tweet
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# if polarity is greater then 0 and less then 5 then tweet is neutral
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tweet_live = self.get_live_tweets_from_Twitter(text)
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result = []
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for tweet in tweet_live:
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polarity = TextBlob(tweet.full_text).sentiment.polarity
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subjectivity = TextBlob(tweet.full_text).sentiment.subjectivity
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score = SentimentIntensityAnalyzer().polarity_scores(tweet.full_text)
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if polarity < 0 or subjectivity < 0 and score['neg'] > score['pos']:
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result.append([tweet.full_text, polarity, subjectivity, score, "negative"])
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elif polarity > 0 and subjectivity > 0 and score['neg'] < score['pos']:
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result.append([tweet.full_text, polarity, subjectivity, score, "positive"])
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else:
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result.append([tweet.full_text, polarity, subjectivity, score, "neutral"])
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return result
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## Twitter Sentimental Analysis Script
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This python script can be used to scrap twitter for a specific tweets according to there hashTags and then classify these tweets according to there sentiments example the tweet is positive, negative or neutral tweet
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This provides features like :
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1. Tweets Scraping
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2. sorting the Tweets
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3. Finding out the polarity,subjectivity score of a tweet
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4. SentimentIntensityAnalyzer on the tweet
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5. based on these scores it differentiate them in different categories like Positive , Negative and more.
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## Steps to Create your Credentials :
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1. Apply for a Twitter Developer Account here [Developers site](https://developer.twitter.com/en)
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2. Create an Application a new Application
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3. Create the Authentication Credentials
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4. Copy and paste your credential_variables.env file
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and done your credentials are setup for working
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## Installation
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First of all install [python]("https://www.python.org/downloads/") on your system.
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```
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pip install nltk
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pip install tweepy
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pip install textblob
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```
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### Made with ❤️ by Shantam Sultania
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You can find me at:-
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[Linkedin](https://www.linkedin.com/in/shantam-sultania-737084175/) or [Github](https://github.com/shantamsultania) .
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Happy coding ❤️ .

‎TwitterTweetSentimentalAnalysis/app.py

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from TwitterTweetSentimentalAnalysis import Model
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import getpass
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class app(object):
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@staticmethod
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def run_app():
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try:
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candidate_key = getpass.getpass(prompt='enter your candidate Key : ')
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candidate_sec = getpass.getpass(prompt='enter your candidate secret Key : ')
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access_key = getpass.getpass(prompt='enter your access Key : ')
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access_sec = getpass.getpass(prompt='enter your access secret Key : ')
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except Exception as E:
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print('There is an Error : ', E)
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else:
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model_object = Model.model(candidate_key, candidate_sec, access_key, access_sec)
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print(model_object.get_authenticated_api())
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text = input(" Enter the tag you want to perform sentimental analysis on : ")
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result = model_object.detailed_analysis_tweet_data(text)
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for i in result:
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print(i)
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if __name__ == "__main__":
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object = app()
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object.run_app()
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nltk==3.2.4
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tweepy==3.10.0
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textblob==0.15.3

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