twitterSentimentalAnalysisScript (avinashkranjan#952)

Author: shantamsultania

TwitterTweetSentimentalAnalysis/Model.py

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+from nltk.sentiment import SentimentIntensityAnalyzer
+from textblob import TextBlob
+
+import tweepy
+
+
+class model(object):
+
+    def __init__(self, candidate_key, candidate_sec, access_key, access_sec):
+        super().__init__()
+        self.candidate_key = candidate_key
+        self.candidate_sec = candidate_sec
+        self.access_key = access_key
+        self.access_sec = access_sec
+
+    def get_authenticated_api(self):
+        auth = tweepy.OAuthHandler(self.candidate_key, self.candidate_sec)
+        auth.set_access_token(self.access_key, self.access_sec)
+        api = tweepy.API(auth)
+        return api
+
+    def get_live_tweets_from_Twitter(self, text):
+
+        api = self.get_authenticated_api()
+
+        tweet_live = api.search(text, tweet_mode='extended')
+        return tweet_live
+
+    def analysis_live_tweet_data(self, text):
+
+        tweet_live = self.get_live_tweets_from_Twitter(text)
+        for tweet in tweet_live:
+            tweet_is = tweet.text
+            analysis = TextBlob(tweet_is)
+
+    def detailed_analysis_tweet_data(self, text):
+
+        # if polarity is in negative then the tweet is negative
+        # if in positive then its a positive tweet
+        # if polarity is greater then 0 and less then 5 then tweet is neutral
+
+        tweet_live = self.get_live_tweets_from_Twitter(text)
+        result = []
+        for tweet in tweet_live:
+            polarity = TextBlob(tweet.full_text).sentiment.polarity
+            subjectivity = TextBlob(tweet.full_text).sentiment.subjectivity
+            score = SentimentIntensityAnalyzer().polarity_scores(tweet.full_text)
+
+            if polarity < 0 or subjectivity < 0 and score['neg'] > score['pos']:
+                result.append([tweet.full_text, polarity, subjectivity, score, "negative"])
+            elif polarity > 0 and subjectivity > 0 and score['neg'] < score['pos']:
+                result.append([tweet.full_text, polarity, subjectivity, score, "positive"])
+            else:
+                result.append([tweet.full_text, polarity, subjectivity, score, "neutral"])
+
+        return result

TwitterTweetSentimentalAnalysis/Readme.md

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+## Twitter Sentimental Analysis Script
+
+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 
+
+This provides features like :
+
+1. Tweets Scraping 
+2. sorting the Tweets 
+3. Finding out the polarity,subjectivity score of a tweet 
+4. SentimentIntensityAnalyzer on the tweet
+5. based on these scores it differentiate them in different categories like Positive , Negative and more.
+
+
+## Steps to Create your Credentials :
+
+1. Apply for a Twitter Developer Account  here [Developers site](https://developer.twitter.com/en)
+2. Create an Application a new Application 
+3. Create the Authentication Credentials
+4. Copy and paste your credential_variables.env file
+
+and done your credentials are setup for working 
+
+
+## Installation
+
+First of all install [python]("https://www.python.org/downloads/") on your system.
+
+```
+pip install nltk 
+pip install tweepy
+pip install textblob
+```
+
+
+### Made with ❤️ by Shantam Sultania
+
+You can find me at:-
+[Linkedin](https://www.linkedin.com/in/shantam-sultania-737084175/) or [Github](https://github.com/shantamsultania) .
+
+Happy coding ❤️ .

TwitterTweetSentimentalAnalysis/app.py

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

TwitterTweetSentimentalAnalysis/requirements.txt

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+nltk==3.2.4
+tweepy==3.10.0
+textblob==0.15.3