Coronavirus disease 2019 (COVID-19) has become a part of our everyday life in the year of 2020.
Many people have turned to online social media platforms to share what they think and how they feel about the sudden impact the pandemic has brought upon us. This project aims to study public attitudes toward COVID-19 on Twitter, a popular social network platform. In particular, it focuses on discovering what issues around COVID-19 people are discussing, why they are interested in such topics, and how their emotions have evolved over time. The study further seeks to reveal potential associations between the breakout and any hidden idea previously unknown to the general public.
The dataset was created by collecting approximately 150,000 tweets with keywords or hashtags related to COVID-19 over a course of four weeks with Python and Twitter API. A comprehensive analysis of the tweets was performed using natural language processing methodologies including topic modeling, sentiment analysis, and word embedding. The results suggest that many people may be failing to practice appropriate safety measures to stop the spread, despite their high interests in the COVID-19 crisis. In other words, their proactive online actions are not influencing their offline, real-life behaviors.
