Before revision:  In this paper, we attempt to shed light on the term's real-world usage on Twitter. Using sentiment feature analysis and topic modeling, we reveal substantial differences between the use of the controversial terms such as "Chinese virus " and that of the non-controversial terms such as "COVID-19" . For example, tweets using controversial terms contain a higher percentage of anger as well as negative emotions.
After revision:  In this paper, we attempt to shed light on the term's real-world usage is lacking to shed light on the issues behind the controversy. In this paper, we attempt to fill this gap. To model the substantive difference of tweets with controversial terms and those with non-controversial terms, we apply topic modeling and LIWC-based sentiment analysis. To test whether "Chinese virus " and that of the non-controversial terms such as "COVID-19" . For example, tweets using controversial terms contain a higher percentage of anger as well as negative emotions.