sentiment_analysis_textblob.py

from textblob import TextBlob

# compute sentiment scores (polarity) and labels
sentiment_scores_tb = [round(TextBlob(article).sentiment.polarity, 3) for article in news_df['clean_text']]
sentiment_category_tb = ['positive' if score > 0 
                             else 'negative' if score < 0 
                                 else 'neutral' 
                                     for score in sentiment_scores_tb]


# sentiment statistics per news category
df = pd.DataFrame([list(news_df['news_category']), sentiment_scores_tb, sentiment_category_tb]).T
df.columns = ['news_category', 'sentiment_score', 'sentiment_category']
df['sentiment_score'] = df.sentiment_score.astype('float')
df.groupby(by=['news_category']).describe()