SASM is a practice of collecting data from social networks and automatically identifying whether a phrase comprehends sentiment or opinionative content, and further determines the opinion polarity (Jianqiang & Xiaolin, 2017). Investigating hidden but potentially useful patterns from a huge collection of SMC is a critical task, due to users struggle with overloaded information (Yang & Rim, 2014). Accordingly, large volumetric semantically rich information is being generated and accumulated every day in the form of tweets, posts, blogs, news, comments, reviews, etc. In recent days, social media applications have emerged as leading mass media, as they allow users to work collaboratively and publish their content (Wadawadagi & Pagi, in press Anami et al. Finally, the research highlights certain issues related to ML used for SMC. Furthermore, the study reports that ML has a significant contribution to SMC mining. This survey presents the basic elements of SASM and its utility. The framework of SASM consists of several phases, such as data collection, pre-processing, feature representation, model building, and evaluation. This chapter studies recent advances in machine learning (ML) used for SMC analysis and its applications. SA on social media (SASM) extends an organization's ability to capture and study public sentiments toward social events and activities in real time. Identifying emotions in SMC is important for many aspects of sentiment analysis (SA) and is a top-level agenda of many firms today. have become an integral part of our lives, as they prompt the people to give their opinions and share information around the world. Social media applications such as Instagram, Twitter, Facebook, etc. AbstractDue 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.
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