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Tracking Emotional Changes in Experimental and Naturalistic Texts Across Waves of the Pandemic

LIWC Research Series:


Early studies on a population’s emotional response to the COVID-19 pandemic found that using text analysis methods can accurately detect and replicate self-reported emotions. Using both naturalistic and experimental writing samples, we further investigate the change in emotions between waves. In this study, we use the Linguistic Inquiry and Word Count (LIWC) and the Syntax Aware LexicaL Emotion Engine (SALLEE) to score text samples according to 9 self-reported emotion categories by participants. Our analysis of experimental texts showed moderate correlations between self-reported emotions and software metrics, with both LIWC and SALLEE replicating the change in emotions between waves. Naturalistic text data (taken from Reddit) replicated experimental text data for most emotions. Across all texts, the topic of vaccination elicited mostly positive sentiment and the topic of government elicited mostly negative sentiment. Studying language with text analysis tools like LIWC and SALLEE can reveal important information about how communities and individuals react to or cope with traumatic events.


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Authors: Emma S. Gueorguieva, Kiki Adams, and Molly E. Ireland, @Receptiviti