Federated Learning Approach for Social Media Sentiment Analysis: Analyzing Public Opinion

This paper examines how federated learning can be applied to social media sentiment analysis, focusing on public opinion across various online platforms. By using federated learning, we combine sentiment analysis models from multiple sources while maintaining data privacy and security. Our experiments show that federated learning successfully captures dynamic sentiment trends across different social media sites. The findings offer valuable insights into public opinion, benefiting businesses, policymakers, and researchers. Future research will explore advanced federated learning techniques, optimize model architectures, include multimodal data, evaluate performance across different demographics and languages, and address scalability for large datasets. This study lays the groundwork for using federated learning in social media sentiment analysis and advancing the understanding of public sentiment in the digital era.