Dramatic Increase in Fear-Related Discussion on Twitter during COVID-19: Analysis, Topic Modeling and Tweets Classification

COVID-19 is one the most lethal virus, causing millions of death to date. It was initially detected in Wuhan, China. It then spread rapidly around the globe, which resultantly created major setbacks in the public health sector. The reason of millions of deaths is not only due to the virus itself but it is also linked to peoples’ mental state, and sentiments triggered by the fear of the virus. These sentiments are predominantly available on posts/tweets on social media. This paper presents a novel approach for exploratory data analysis of twitter to understand the emotions of general public; country wise, and user wise. Firstly K-Means clustering is employed for topic modeling to categorize the emotions in each tweet. Further supervised machine learning techniques are used to categorize the multi-label tweets. This research concluded that Fear was the most common emotion in twitter discussion. Furthermore, we classified the dataset by performing decision tree (DT), logistic regression (LR), and support vector machine (SVM), finally this paper concluded the results of classification, which shows that SVM can attain better classification accuracy (99%) for COVID-19 text classification.