With the aid of the NLP library, nltk, I made a visualization displaying the most positive and negative words in post titles of WallStreetBets.
I assigned positive and negative variables to appropriate columns with the corresponding sentiment. After that, I defined and updated the list of stop words being used to fit our scenario. There are sarcastic comments, trolls, and even profanity used at times in the threads for WallStreetBets so I would like to keep that in mind when doing analysis. I used a wordcloud with stopwords that exclude the words great and good because they were also included in negative sentiment.
Next, I utilized a similar approach but with a couple changes. I used a different variable to input different arguments into the wordcloud code. The stopwords and plot settings were the same.
It is up to ones interpretation what is most interesting from this analysis. I find joy in communicating data in a way where we all can look at something on a basic level with different perspectives that add value.