Topic Detection in Sentiment Analysis of Twitter Texts for Understanding The COVID-19 Effect in Local Economic Activities

Abstract

During the COVID-19 situation, discussions about the effect of COVID-19 increase on Twitter. Not only affecting the health sector, but the COVID-19 pandemic has also affected other fields, such as economic activities. Issues related to the economy become an essential discussion on Twitter because this sector has close links with other sectors in public activities. It makes twitter relevant as a knowledge extraction medium to identify users' opini comparisons. The contribution of this research is to find the effect of the COVID-19 pandemic on the comparison of sentiment and emotion in three different locations in Surabaya. Based on the results of emotion detection, at the beginning of the COVID-19 pandemic, topics related to economic activities and personal activities were dominated by anger emotion in the ITS campus and the TP mall area. Then, despite the gradual decrease in the intensity of tweets, the dominance of anger emotion tends to be stable. On economics topics, 40% of tweets in the ITS campus area and 84% of tweets in the TP mall area were dominated by anger emotion. Then 37% of tweets in the ITS campus area and 32% tweets in the Tunjungan Plaza mall area based on personal activities were dominated by anger. The economics topic is related to buying-selling and shopping activities, while personal activity is related to lifestyle and daily activities. These results indicate that during the COVID-19 pandemic, anger became the most dominant sentiment related to local economic activity from Twitter users in Surabaya.