topic between young family real performance beautiful work each both director topic war world us our american documentary history new own point topic funny worst comedy thing guy re stupid actually nothing want topic show series episode tv episodes shows season new television years topic didn saw am thought years book watched now dvd got topic horror action effects budget nothing original director minutes pretty doesn topic kids action animation game fun disney children kid old topic cast role john version novel both director played performance mr topic performance role john actor oscar cast plays jack joe performances topic house woman gets killer girl wife horror young goes around judging from the important wordstopic seems to be about historical and war moviestopic might be about bad comediestopic might be about tv series topic seems to capture some very common wordswhile topic appears to be about children' movies and topic seems to capture award-related reviews using only topicseach of the topics needs to be very broadso that they can together cover all the different kinds of reviews in our dataset nextwe will learn another modelthis time with topics using more topics makes the analysis much harderbut makes it more likely that topics can specialize to interesting subsets of the datain[ ]lda latentdirichletallocation(n_topics= learning_method="batch"max_iter= random_state= document_topics lda fit_transform(xlooking at all topics would be bit overwhelmingso we selected some interesting and representative topics working with text data