although daunting at first glancewe can clearly see that the topics are not just random wordsbut are connected we can also see that these topics refer to older news itemsfrom when the soviet union still existed and gorbachev was its secretary general we can also represent the topics as word cloudsmaking more likely words larger for examplethis is the visualization of topicwhich deals with the middle east and politicswe can also see that some of the words should perhaps be removed (for examplethe word ias they are not so informative (stop wordsin topic modelingit is important to filter out stop wordsas otherwise you might end up with topic consisting entirely of stop wordswhich is not very informative we may also wish to preprocess the text to stems in order to normalize plurals and verb forms this process was covered in the previous and you can refer to it for details if you are interestedyou can download the code from the companion website of the book and try all these variations to draw different pictures building word cloud like the one in the previous screenshot can be done with several different pieces of software for the previous graphici used the online tool wordle (which generates particularly attractive images since only had few examplesi copy and pasted the list of words manuallybut it is possible to use it as web service and call it directly from python comparing similarity in topic space topics can be useful on their own to build small vignettes with words that are in the previous screenshot these visualizations could be used to navigate large collection of documents andin factthey have been used in just this way