GSAF: An ML-Based Sentiment Analytics Framework for Understanding Contemporary Public Sentiment and Trends on Key Societal Issues

Abstract

This paper presents a Generalized Sentiment Analytics Framework (GSAF) for understanding public sentiments on different key societal issues in real time. The framework uses natural language processing techniques for computing sentiments and displays them in different emotions leveraging publicly available social media data (i.e., X threads (formally Twitter)). As a case study of our developed framework, we have leveraged over 3 million tweets to map, analyze, and visualize public sentiment state-wise across the United States on different societal issues. With X as a key social media platform, this study harnesses its vast user base to provide real-time insights into emotional responses surrounding key societal and political events. Built using R and the Shiny web framework, the platform offers users interactive visualizations of emotion-specific sentiments, such as anger, joy, and trust, displayed on a U.S. state-level choropleth map. The platform allows keyword-based searches and employs advanced text-processing techniques to filter and clean tweet data for robust analysis. Furthermore, it implements efficient caching mechanisms to enhance performance, comparing various strategies like LRU and Size-Based Eviction. This research highlights the potential of sentiment analysis for policymaking, marketing, and public discourse, providing a valuable tool for understanding and predicting public sentiment trends.