gistfile1.txt

#implementing the Vader Sentiment Analysis, handily for almost the totality of the simple sentiment analysis
import nltk
nltk.download('vader_lexicon')
from nltk.sentiment.vader import SentimentIntensityAnalyzer
def sentiment_scores(sentence):
 
    # Create a SentimentIntensityAnalyzer object.
    sid_obj = SentimentIntensityAnalyzer()

    # the sentiment dictionary contains pos, neg, neu, and compound scores.
    sentiment_dict = sid_obj.polarity_scores(sentence)
    print(sentiment_dict['neg']*100)
    print(sentiment_dict['neu']*100)
    print(sentiment_dict['pos']*100)

def sentiment_dictneg(phrase): 
   sid_obj = SentimentIntensityAnalyzer()
   sentiment_dict = sid_obj.polarity_scores(phrase)
   return sentiment_dict['neg']*100

def sentiment_dictneu(phrase): 
   sid_obj = SentimentIntensityAnalyzer()
   sentiment_dict = sid_obj.polarity_scores(phrase)
   return sentiment_dict['neu']*100 

def sentiment_dictpos(phrase): 
   sid_obj = SentimentIntensityAnalyzer()
   sentiment_dict = sid_obj.polarity_scores(phrase)
   return sentiment_dict['pos']*100