we depict some of the topics out of the topics generated in the preceding output you can leverage pyldavis now to visualize these topics in an interactive visualization see figure - in [ ]pyldavis sklearn prepare(pos_nmfptvf_featuresptvfr= figure - visualizing topic models on positive sentiment movie reviews the visualization depicted in figure - shows us the topics from positive movie reviews and we can see the top relevant terms for topic highlighted in the output from the topics and the termswe can see terms like movie castactorsperformanceplaycharactersmusicwonderfulgoodand so on have contributed toward positive sentiment in various topics this is quite interesting and gives you good insight into the components of the reviews that contribute toward positive sentiment of the reviews this visualization is completely interactive if you are using the jupyter notebook and you can click on any of the bubbles representing topics in the intertopic distance map on the left and see the most relevant terms in each of the topics in the right bar chart the plot on the left is rendered using multi-dimensional scaling (mdssimilar topics should be close to one another and dissimilar topics should be far apart the size of each topic bubble is based on the frequency of that topic and its components in the overall corpus the visualization on the right shows the top terms when no topic it selectedit shows the top most salient topics in the corpus term' saliency is defined as measure of how frequently the term appears the corpus and its distinguishing factor when used to distinguish between topics when some topic is selectedthe chart changes to show something similar to figure - which shows the top most relevant terms for that topic the relevancy metric is controlled by lwhich can be changed based on slider on top of the bar chart (refer to the notebook to interact with thisif you're interested in more mathematical theory behind these visualizationsyou are encouraged to check out more details at packages/ldavis/vignettes/details pdfwhich is vignette for the package ldaviswhich has been ported to python as pyldavis