Enhancing Social Media Analytics with Machine Learning Techniques

Abstract

The surge in social media usage has generated vast amounts of unstructured data, offering unique opportunities and challenges for data-driven insights. Machine learning (ML) provides powerful tools to analyze, predict, and optimize user behavior and trends in social media analytics. This paper explores how ML techniques, including supervised and unsupervised learning, natural language processing (NLP), and deep learning, enhance various aspects of social media analysis such as sentiment detection, user segmentation, and predictive modeling. We present recent advancements and case studies, highlighting the practical applications and limitations of current approaches.