text mining and text analytics two ipython notebook files are available for this data collection--will contain the data collection part of this case study data preparation and analysis--the stored data is put through data preparation and then subjected to analysis all code in the upcoming case study can be found in these two files in the same sequence and can also be run as such in additiontwo interactive graphs are available for downloadforcegraph html--represents the top features of our naive bayes model sunburst html--represents the top four branches of our decision tree model to open these two html pagesan http server is necessarywhich you can get using python and command windowopen command window (linuxwindowswhatever you fancymove to the folder containing the html files and their json data filesdecisiontreedata json for the sunburst diagram and naivebayesdata json for the force graph it' important the html files remain in the same location as their data files or you'll have to change the javascript in the html file create python http server with the following commandpython - simplehttpserver open browser and go to localhost: here you can select the html filesas shown in figure figure python http server serving this output the python packages we'll use in this nltk--for text mining praw--allows downloading posts from reddit sqlite --enables us to store data in the sqlite format matplotlib-- plotting library for visualizing data make sure to install all the necessary libraries and corpora before moving on before we dive into the actionhoweverlet' look at the steps we'll take to get to our goal of creating topic classification model