let' now extract topics and run this same analysis on our negative sentiment reviews from the movie reviews dataset in [ ]build topic model on negative sentiment review features neg_nmf nmf(n_components= random_state= alpha= _ratio= neg_nmf fit(ntvf_featuresextract features and component weights neg_feature_names ntvf get_feature_names(neg_weights neg_nmf components_ extract and display topics and their components neg_topics tmu get_topics_terms_weights(neg_weightsneg_feature_namestmu print_topics_udf(topics=neg_topicstotal_topics=total_topicsnum_terms= display_weights=falsetopic # without weights ['get''go''kill''guy''scene''take''end''back''start''around''look''one''thing''come''first'topic # without weights ['bad''movie''ever''acting''see''terrible''one''plot''effect''awful''not''even''make''horrible''special'topic # without weights ['waste''time''money''watch''minute''hour''movie''spend''not''life''save''even''worth''back''crap'in [ ]pyldavis sklearn prepare(neg_nmfntvf_featuresntvfr= figure - visualizing topic models on positive sentiment movie reviews