An Extensive Review of Sentiment Analysis Leveraging Artificial Intelligence and Machine Learning

Because social networking sites are growing so quickly in the Internet age, they are now a vital tool for sharing emotions with people everywhere. Many people convey their opinions or thoughts using text, images, music, and video. On the other hand, text communication through Web-based networking media might be a little overwhelming. Social media platforms are the source of vast amounts of unstructured data generated on the Internet every second. To understand human psychology, data must be analysed as quickly as it is generated. Sentiment analysis, which identifies polarity in texts, can help with this. It evaluates the author’s attitude toward a thing, organization, person, or place—whether it be favourable, negative, or neutral. Organizations can obtain real-time insights on customer sentiment, experience, and brand reputation with the use of sentiment analysis technologies. These technologies typically evaluate online sources like emails, blogs, reviews, customer service tickets, news stories, survey results, case studies, web chats, tweets, forums, and comments using text analytics. Whether the consumer is using positive, negative, or neutral language, algorithms are employed to construct rule-based, automatic, or hybrid methods of scoring. Sentiment analysis may not be sufficient in certain applications; in such cases, emotion detection—which accurately assesses a person’s emotional and mental state—is necessary. Understanding of sentiment analysis levels, different emotion models, and the procedure for sentiment analysis and emotion detection from text are all provided by this review work. This paper concludes by discussing the difficulties encountered in sentiment and emotion analysis.