Climate Mood

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.

Go to the dashboard

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.


Climate Trends

Been thinking about climate a bunch lately and started looking into trying out the latest data tools and trends out there on the Internets. I’ve particularly enjoyed R and Pandas/Python, especially via Plotly Dash, and re-applying some of the methodologies and statistical analyses I’ve learned while on the job. The latest curiosity is in global temperature change since 1910 and various other societal factors. Lo and behold, the first iteration and not-so-polished, Climate Trends.

You can view the dashboard here

The dashboard has a drop-down menu of various analyses around global temperature change and linear fits across birth rates, fertility rates, death rates, life expectancy at birth, and rural population growth rate as well as a nominal years passed chart. Feel free to dabble!

If you have any feedback or suggestions, please submit an issue on the GitHub repo, respond on Dribbble, or contact me.