Machine learning-based Sentiment Analysis: A Comprehensive Review

Opinion mining, also known as sentiment analysis (SA), aims to determine the thoughts and comments of others, and it has been made possible by the rapid growth of internet users, the increasing influence of online review sites, and social media. One popular area of research is sentiment analysis of online reviews, which includes user-generated information about goods, productivity, services, laws, and politics. People are often curious to learn about others' likes and dislikes, including both positive and negative remarks, for specific aspects of a product or service. Text mining research is continually conducted in the field of sentiment analysis due to the multiple uses of subjective texts. Sentiment analysis involves algorithmically analyzing a text’s subjectivity, emotions, opinions, and knowledge sharing. This survey study provides an in-depth analysis of the latest advancements in this area and discusses the methodologies and techniques currently used in feature extraction for sentiment mining, collaborative decision, and opinion mining. In this review, a systematic literature review approach is utilized to identify the research areas that have received the most attention from researchers. This study demonstrates the current research trend in sentiment analysis through the detailed categorization of many recent papers, and the main contributions of this work are the interconnected domains.