Methods and Applications for Sentiment Analysis in Online Social Networks

With hyped popularity of social media, sentiment analysis, a branch of natural language processing (NLP), has gained significant attention. Social media is a treasure trove for gaining insights into the thoughts, feelings, and opinions of the general public due to the abundance of user-generated content. This work offers a thorough review of sentiment analysis applied in social media, covering its methods, applications, and challenges. It discusses various approaches to sentiment analysis, from deep learning and machine learning models to rule-based techniques. Additionally, it examines the challenges associated with sentiment analysis of social media content, such as context dependence, slang, and sarcasm. The study also explores the various applications of sentiment analysis in social media, including tracking public opinion, political sentiment analysis, customer feedback analysis, and brand monitoring. By exploring these facets, this work seeks to throw light on the current state of the art in social media sentiment analysis and its implications.