Sentiment Analysis for Twitter Chatter During the Early Outbreak Period of COVID-19
Fahed Jubair, Nesreen A. Salim, Omar Al‐karadsheh, Yazan Mansour Hassona, Ramzi Saifan, Mohammad Abdel-Majeed · 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) · 2021
This study presents the findings of applying sentiment analysis on a corpus of seven million unique English tweets collected from March 26, 2020 to April 9, 2020 about the COVID-19 outbreak. First, an off-the-shelf lexicon-based sentiment analysis tool was used to determine sentiment polarity in each tweet. Then, an off-the-shelf text visualization tool was used to visualize the most frequent emotions and topics that showed positive and negative sentiments. The study revealed meaningful insights about which positive and negative emotion types were most prominent on Twitter chatter during the early period of the COVID-19 pandemic, and which topics garnered the most positive and negative emotional reactions. This work shows that analyzing social media chatter using sentiment analysis and text visualization tools is an effective approach for tracking people's concerns and mental health during pandemics and infectious diseases outbreaks.