| Abstract |
Social sentiments are invaluable sources of information that offer insights into public
opinion, consumer behavior, brand perception, crisis management, political analysis, and societal
trends. By leveraging sentiment analysis techniques, businesses, governments, and organizations
can extract actionable insights from social media data to inform decision-making, improve
engagement, and enhance stakeholder relationships. Analyzing social sentiments involves
understanding the underlying sentiments and moods prevalent in social interactions, which can
provide valuable insights into public opinion, consumer behavior, political trends and dynamics
of society.The rapid expansion of social media platforms has resulted in an unparalleled surge in
user-generated content, emphasizing the critical importance of sentiment analysis and trend
identification for gaining insights into societal trends and behaviors. This paper presents a
comprehensive review of machine learning techniques employed for analyzing social sentiments
and identifying trends in social media data. This review seeks to amalgamate findings from current
literature to offer researchers and practitioners a fundamental grasp of sentiment analysis and
trend detection in social media. By doing so, it sets the stage for future developments in this swiftly
changing domain. By examining a range of machine learning methods, preprocessing approaches,
and models utilized in this area, the paper provides a thorough understanding of the field. It also
tackles issues such as data noise and ethical concerns while pinpointing prospects for innovation.
Ultimately, the aim of this review is to chart a course for future progress in the field by outlining
critical research paths and promoting interdisciplinary cooperation.
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