ABSTRACT
“Emotions and views of people have become important factors in communication and decisionmaking in the age of computer science and information technology. Because web documents are machine-readable and easily accessible, there is a growing interest in leveraging these feelings in the context of consumer evaluations and weblogs. This increase is in line with developments in information retrieval (IR), machine learning (ML), and natural language processing (NLP), which have made accessible techniques for gathering and evaluating opinions. With an emphasis on web papers, this extensive article explores Opinion Mining and Sentiment Analysis (OMSA). It sheds light on the changing area of sentiment analysis and makes recommendations for future research directions by examining important ideas, technical developments, research obstacles, and current solutions. This study's scope is broad and includes anything from explaining basic terms to assessing the effects of technology, resolving issues, and suggesting new research directions. Its importance is extensive, as it advances knowledge in academia, real-world applications, technology, information retrieval, business and marketing tactics, and other areas. Technically speaking, this effort highlights the critical role that OMSA plays in algorithmic decision-making and computational science by providing a thorough computational exploration of the topic. In addition, the influence of technology developments in IR, NLP, and ML is examined, emphasizing their important contributions to OMSA. These developments have enhanced the OMSA systems' scalability, accuracy, efficiency, and cost-effectiveness. These days, OMSA systems—which use machine learning (ML) algorithms—are crucial for comprehending the wide range of online attitudes and opinions, which has significant ramifications for companies and organizations in a variety of sectors.”
