topics np array([ ]sorting np argsort(lda components_axis= )[:::- feature_names np array(vect get_feature_names()mglearn tools print_topics(topics=topicsfeature_names=feature_namessorting=sortingtopics_per_chunk= n_words= out[ ]topic thriller suspense horror atmosphere mystery house director quite bit de performances dark twist hitchcock tension interesting mysterious murder ending creepy topic worst awful boring horrible stupid thing terrible script nothing worse waste pretty minutes didn actors actually re supposed mean want topic german hitler nazi midnight joe germany years history new modesty cowboy jewish past kirk young spanish enterprise von nazis spock topic car gets guy around down kill goes killed going house away head take another getting doesn now night right woman topic beautiful young old romantic between romance wonderful heart feel year each french sweet boy loved girl relationship saw both simple topic performance role actor cast play actors performances played supporting director oscar roles actress excellent screen plays award work playing gives topic excellent highly amazing wonderful truly superb actors brilliant recommend quite performance performances perfect drama without beautiful human moving world recommended topic war american world soldiers military army tarzan soldier america country americans during men us government jungle vietnam ii political against topic music song songs rock band soundtrack singing voice singer sing musical roll fan metal concert playing hear fans prince especially topic earth space planet superman alien world evil humans aliens human creatures miike monsters apes clark burton tim outer men moon topic modeling and document clustering