Meta-Learning in Textual Sentiment and Emotion Analysis: A Comprehensive Review

As a result of the rapid development of the Internet era and the increasing prevalence of Internet use, social networking platforms have become an essential means of exchanging feelings with people worldwide. The ability to submit real-time messages representing their opinions on various subjects, talk about common problems or complaints, and express their feelings about anything that matters is available to web users. Social media networks generate a substantial amount of unstructured data on the Internet each and every second. Many people are interested in understanding human psychology because of the abundance of data. Sentiment analysis is used, which determines if the person may have negative, positive, or neutral thoughts on an incident. Emotion detection, which determines a person’s emotional state, is necessary in particular situations where the use of sentiment analysis is insufficient. Deep learning has become the most popular sentiment analysis and emotion recognition technique. However, the need for many tagged data to achieve better performances demonstrates a more effective strategy to overcome this restriction. An emerging topic, "Meta-learning", seeks to enhance algorithms in varied ways, which include generalizability and data efficiency. This review study offers insight into multiple sentiment analysis levels, diverse emotion models, fundamental meta-learning ideas, an analysis of sentiment analysis and emotion detection techniques and future directions.