:- [- - - - - - - - - : [ terrorizing - [- - - - - - - - - - thankful [ each line in the preceding lexicon sample depicts unique termwhich can either be an emoticon or word the first token indicates the word/emoticonthe second token indicates the mean sentiment polarity scorethe third token indicates the standard deviationand the final token indicates list of scores given by ten independent scorers now let' use vader to analyze our movie reviewswe build our own modeling function as follows from nltk sentiment vader import sentimentintensityanalyzer def analyze_sentiment_vader_lexicon(reviewthreshold= verbose=false)pre-process text review tn strip_html_tags(reviewreview tn remove_accented_chars(reviewreview tn expand_contractions(reviewanalyze the sentiment for review analyzer sentimentintensityanalyzer(scores analyzer polarity_scores(reviewget aggregate scores and final sentiment agg_score scores['compound'final_sentiment 'positiveif agg_score >thresholdelse 'negativeif verbosedisplay detailed sentiment statistics positive str(round(scores['pos'] )* )+'%final round(agg_score negative str(round(scores['neg'] )* )+'%neutral str(round(scores['neu'] )* )+'%sentiment_frame pd dataframe([[final_sentimentfinalpositivenegativeneutral]]columns=pd multiindex(levels=[['sentiment stats:']['predicted sentiment''polarity score''positive''negative''neutral']]labels=[[ , , , , ],[ , , , , ]])print(sentiment_framereturn final_sentiment in our modeling functionwe do some basic pre-processing but keep the punctuations and emoticons intact besides thiswe use vader to get the sentiment polarity and also proportion of the review text with regard to positiveneutral and negative sentiment we also predict the final sentiment based on user-input threshold for the aggregated sentiment polarity typicallyvader recommends using positive sentiment