actual sentimentpositive sentiment statspredicted sentiment objectivity positive negative overall positive we can clearly see the predicted sentiment along with sentiment polarity scores and an objectivity score for each sample movie review depicted in formatted dataframes let' use this model now to predict the sentiment of all our test reviews and evaluate its performance threshold of >= has been used for the overall sentiment polarity to be classified as positive and for negative sentiment see figure - in [ ]predicted_sentiments [analyze_sentiment_sentiwordnet_lexicon(reviewverbose=falsefor review in norm_test_reviewsmeu display_model_performance_metrics(true_labels=test_sentimentspredicted_labels=predicted_sentimentsclasses=['positive''negative']figure - model performance metrics for sentiwordnet lexicon based model we get an overall -score of %which is definitely step down from our afinn based model while we have lesser number of negative sentiment based reviews being misclassified as positivethe other aspects of the model performance have been affected vader lexicon the vader lexicondeveloped by huttois lexicon that is based on rule-based sentiment analysis frameworkspecifically tuned to analyze sentiments in social media vader stands for valence aware dictionary and sentiment reasoner details about this framework can be read in the original paper by huttoc gilberte ( titled "vadera parsimonious rule-based model for sentiment analysis of social media text"proceedings of the eighth international conference on weblogs and social media (icwsm- you can use the library based on nltk' interface under the nltk sentiment vader module besides thisyou can also download the actual lexicon or install the framework from vadersentimentwhich also contains detailed information about vader this lexiconpresent in the file titled vader_lexicon txt contains necessary sentiment scores associated with wordsemoticons and slangs (like wtflolnahand so onthere were total of over lexical features from which over curated lexical features were finally selected in the lexicon with proper validated valence scores each feature was rated on scale from "[- extremely negativeto "[ extremely positive"with allowance for "[ neutral (or neithern/ )the process of selecting lexical features was done by keeping all features that had non-zero mean rating and whose standard deviation was less than which was determined by the aggregate of ten independent raters we depict sample from the vader lexicon as follows