get_VADAR.py

import pandas as pd
import time
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer


def sentiment_scores(sentence):
 
    # Create a SentimentIntensityAnalyzer object.
    sid_obj = SentimentIntensityAnalyzer()
 
    sentiment_dict = sid_obj.polarity_scores(sentence)

    return sentiment_dict
     

if __name__ == '__main__':
    df = pd.read_csv('manual_filtered_travis_tweets_with_blockgroups_with_scores.csv')
    sentiments = []
    pos = []
    neg = []
    nue = []
    for tweet in df['tweet_text']:
        try:
            score = sentiment_scores(tweet)
            comp = score['compound']
            print(score)
            sentiments.append(comp)
            pos.append(score['pos'])
            neg.append(score['neg'])
            nue.append(score['neu'])
        except:
            sentiments.append('N/A')  # Append 'N/A' if score extraction fails


    df['VADAR_sentiment'] = sentiments  # Add the bot scores to the DataFrame as a new column
    df['postive_score'] = pos 
    df['negative_score'] = neg 
    df['neutral_score'] = nue 
    df.to_csv('manual_filtered_travis_tweets_with_blockgroups_with_scores_and_detailed_sentiment.csv', index=False)  # Save the DataFrame with scores to a new CSV file