Abstract

In recent years, social media has become a vast source of user-generated data that reflects public opinions, emotions, and trends. Analyzing this data using Natural Language Processing (NLP) techniques provides valuable insights for organizations, governments, and researchers. This paper explores different NLP techniques used for analyzing social media data, including sentiment analysis, topic modeling, and emotion detection. The research also highlights the applications and challenges of NLP in understanding public sentiment and making data-driven decisions.