Continuing with the delve into Climate Change, looked into understanding how people feel, positive/neutral/negative, about the subject on Twitter. Using TextBlob, with Tweepy, Pandas, and Plotly Dash…I built initial revision of a data visualization dashboard, Climate Mood, that analyzes tweets over a sample of the last 2,000 collected hourly. The idea is to provide a simple, sentiment-focused look across multiple analyses. I’ve particularly enjoyed building and re-applying some of the methodologies and statistical analyses I’ve learned recently.
As mentioned, the dashboard pulls from Twitter over an hourly basis and deposits into a PostgreSQL database. The top chart provides a linear regression trend line (unevenly for now) across tweets collected that can be toggled. Also, tweets can be seen on the top-right tweet-box on hover. Other simple sentiment analyses include average verbosity of tweet length, popularity of tweets, and average sentiment-based amplitude of tweets. Feel free to check it out!
Looking forward to applying more advanced statistical analyses including a live Monte Carlo Simulation calculation across these auto-updated samples…among others. If you have any feedback or suggestions, please submit an issue on the GitHub repo or contact me.