Understanding Sentiment Polarities and Emotion Categories of People on Public Incidents With the Relation to Government Policies

Public incidents necessitate prompt proactive measures by the government and pertinent departments, posing substantial challenges to emergency management capabilities. With the advancement of internet technologies, social media platforms have played a pivotal role in shaping the landscape of public incidents, progressively emerging as primary conduits for authentic expression and sentiment sharing among individuals. The sentiment polarities and emotion categories manifested on social media platforms serve as the correspondence to real-world societal behaviors and performance. This article presents a novel framework leveraging the Transformer-based pretrained language model for conducting large-scale analysis of publicly available short text data sourced from social media platforms. The research aims to comprehensively understand the dynamic fluctuations across fourteen dimensions of sentiment polarities and emotion categories extracted from short text data expressed by people on public incidents over temporal periods. The study seeks to elucidate the relation between the enactment of relevant policies and the observed sentiment polarities as well as emotion categories. One sentiment polarity and two emotion categories related to policies on public incidents are outlined. This research contributes to the government and pertinent departments by providing insights into the text content on social media platforms concerning public incidents, thereby facilitating the understanding of the evolving sentiment polarities and emotion categories.