Data Analytics
Presenter(s): David Aaron, Eric Wasserman, Aleksei Furlong, John Flees
Advisor(s): Dr. Michael Fahy
The main objective of our project is to develop a method of determining what course of action the public or the government should take in order to quell the effects and spread of the COVID-19 virus, and conceivably any future pandemic. Through the use of Twitter’s available API’s, we can perform sentiment analysis on the public opinion of specific current events; particularly, we are concerned with how sentiment changes as a result of new governmental actions, policies, or other measures. Using the program we make as means of analysis, we aim to evaluate how the public’s sentiment changes as they are updated on the spread of the virus. We hypothesize that there exists a quantifiable relationship between public sentiment and the containment status of the virus. To conduct the research, we’ll be using the programming language, Python, along with the various libraries at our disposal for grabbing sentiment and creating visualizations and depictions of our data.