Crowdsourced Intelligence: A Federated Learning Approach to Analyzing Public Opin-ion on Social Media

Abstract

This research paper explores the application of federated learning to social media sentiment analysis, focusing on the task of analyzing public opinion across diverse online platforms. Making use of federated learning, we aggregate sentiment analysis models trained on data from multiple sources while preserving data privacy and security. Our experiments demonstrate promising results, indicating that federated learning effectively captures dynamic sentiment expressions across various social media platforms. Insights gained from this research contribute to a deeper understanding of public opinion dynamics online, offering valuable implications for businesses, policymakers, and researchers. Future work includes investigating advanced federated learning techniques, optimizing model architectures, extending analysis to multimodal data sources, evaluating generalizability across demographic groups and languages, and assessing scalability for large-scale datasets. This research represents a foundational exploration of federated learning for social media sentiment analysis, with potential implications for advancing both federated learning methodologies and understanding public sentiment in the digital age.