Text Mining Approach to Quantify Consumer Psychology and Behavior in COVID-19 Pandemic

An Application of J-LIWC, J-MFD, and Word-coocurrence Networks

Abstract

On social media, a variety of information is posted and shared in real time on a daily basis. Many of these posts relate to the impact of the COVID-19 pandemic and consumer life. Such spontaneous “real voices” (texts) from consumers contain potential signals for quantitatively understanding consumer psychology and behavior in the pandemic. In this study, we quantify consumer psychology and behavior from large-scale social data on Twitter, and discuss analytical methods and cases to gain insights into the resale phenomenon in the pandemic. Specifically, by applying our psychological category dictionary “J-LIWC” and moral foundation dictionary “J-MFD” to the above data, we were able to visualize that different consumer emotions were evoked depending on the types of product resold, such as toilet paper or masks, and what kind of moral violation consumers perceived about the resale. Furthermore, by analyzing the above data using a word co-occurrence network, we observed trends in products resoled and changes in purchasing behavior linked to the pandemic’s progression. These findings provide a hint for reconstructing the society and economy toward the post-corona era.