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Sentiment Analysis on Social Media: Recent Trends in Machine Learning
Abstract
Due to the advent of Web 2.0, the size of social media content (SMC) is growing rapidly and likely to increase faster in the near future. Social media applications such as Instagram, Twitter, Facebook, etc. have become an integral part of our lives, as they prompt the people to give their opinions and share information around the world. Identifying emotions in SMC is important for many aspects of sentiment analysis (SA) and is a top-level agenda of many firms today. SA on social media (SASM) extends an organization's ability to capture and study public sentiments toward social events and activities in real time. This chapter studies recent advances in machine learning (ML) used for SMC analysis and its applications. The framework of SASM consists of several phases, such as data collection, pre-processing, feature representation, model building, and evaluation. This survey presents the basic elements of SASM and its utility. Furthermore, the study reports that ML has a significant contribution to SMC mining. Finally, the research highlights certain issues related to ML used for SMC.
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