Navigating Sentiment Complexity: Exploring the Rational-Emotional Spectrum and Intergroup Dynamics in Social Media Engagement

Sentiment analysis has been widely used to study the effects of messages on social media. However, prior research has primarily focused on the positive or negative tone, overlooking the rational-emotional dimension embedded within these messages. According to dual-process models of thinking, this overlooked dimension may influence how information is processed. This study introduces the rational-emotional spectrum into sentiment analysis and examines how its interaction with negative-positive intensity and intergroup communication affects the popularity of political posts on social media. We compiled a dataset of approximately 400,000 tweets posted by U.S. congressional politicians between February and August 2021 and employed multiple deep learning models to estimate the negative-positive intensity, rational-emotional spectrum, and intergroup communication score for each tweet. We will apply a Deep Instrumental Variables model to assess the relationships among these factors and expect to find that negative emotional expressions are associated with increased popularity—particularly in intergroup interactions.