How Users' Personality Traits Predict Sentiment Tendencies of User-Generated Content in Social Media: A Mixed Method of Configuration Analysis and Machine Learning.

Objective: Social media content created by users with different personality traits presents various sentiment tendencies, easily leading to irrational public opinion. This study aims to explore the relationships between users' personality traits and sentiment tendencies of user-generated content (UGC).

Method: We crawled 18,686 tweets of 1, 215 users from Twitter to figure out the relationships between personality traits and sentiment tendencies. This study utilizes Essays and Sentiment datasets to train machine learning models for the identification of personality traits and sentiment tendencies and then explores the configuration effect of personality traits on sentiment tendency via crisp-set Qualitative Comparative Analysis (csQCA).

Result: The findings suggest that (1) one-dimensional personality trait is not a necessary condition for the sentiment tendencies of UGC. (2) There are multiple equivalent configurations that lead to the sentiment tendencies of UGC.

Conclusion: The study suggests that the sentiment tendencies pattern of UGC can be discovered via the configurations of various dimensions of personality traits.