COVID-19 pandemic and the economy: sentiment analysis on Twitter data

In the last decade, social networks have increasingly been used in social sciences to monitor consumer preferences and citizens' opinion formation, as they are able to produce a massive amount of data. In this paper, we aim to collect and analyse data from Twitter posts identifying emerging patterns related to the COVID-19 outbreak and to evaluate the economic sentiment of users during the pandemic. Using the Twitter API, we collected tweets containing the term coronavirus and at least a keyword related to the economy selected from a pre-determined batch, obtaining a database of approximately two million tweets. We show that our Economic Twitter Index (ETI) is able to nowcast the current state of economic sentiment, exhibiting peaks and drops related to real-world events. Finally, we test our index and it shows a positive correlation to standard economic indicators.