for aggregated polarity > neutral between [- ]and negative for polarity - we use threshold of > for positive and for negative in our corpus the following is the analysis of our sample reviews in [ ]for reviewsentiment in zip(test_reviews[sample_review_ids]test_ sentiments[sample_review_ids])print('review:'reviewprint('actual sentiment:'sentimentpred analyze_sentiment_vader_lexicon(reviewthreshold= verbose=trueprint('-'* reviewno comment stupid movieacting average or worse screenplay no sense at all skip itactual sentimentnegative sentiment statspredicted sentiment polarity score positive negative neutral negative - reviewi don' care if some people voted this movie to be bad if you want the truth this is very good movieit has every thing movie should have you really should get this one actual sentimentpositive sentiment statspredicted sentiment polarity score positive negative neutral negative - reviewworst horror film ever but funniest film ever rolled in one you have got to see this film it is so cheap it is unbelievable but you have to see it really!!! watch the carrot actual sentimentpositive sentiment statspredicted sentiment polarity score positive negative neutral positive we can see the details statistics pertaining to the sentiment and polarity for each sample movie review let' try out our model on the complete test movie review corpus now and evaluate the model performance see figure - in [ ]predicted_sentiments [analyze_sentiment_vader_lexicon(reviewthreshold= verbose=falsefor review in test_ reviewsmeu display_model_performance_metrics(true_labels=test_sentimentspredicted_labels=predicted_sentimentsclasses=['positive''negative']figure - model performance metrics for vader lexicon based model