Ruis, Andrew R.
; Lee, Seung B.
While there has been much growth in the use of microblogging platforms (e.g., Twitter) to share information on a range of topics, researchers struggle
to analyze the large volumes of data produced on such platforms. Established methods such as Sentiment Analysis (SA) have been criticized over their inaccuracy
and limited analytical depth. In this exploratory methodological paper, we propose
a combination of SA with Epistemic Network Analysis (ENA) as an alternative
approach for providing richer qualitative and quantitative insights into Twitter
discourse. We illustrate the application and potential use of these approaches by
visualizing the differences between tweets directed or discussing Democrats and
Republicans after the COVID-19 Stimulus Package announcement in the US. SA
was integrated into ENA models in two ways: as a part of the blocking variable
and as a set of codes. Our results suggest that incorporating SA into ENA allowed
for a better understanding of how groups viewed the components of the stimulus
issue by splitting them by sentiment and enabled a meaningful inclusion of data
with singular subject focus into the ENA models.