A Review on Emotion and Sentiment analysis Using Learning Techniques

Abstract

Abstract: Social media sites have become vital instruments for spreading ones personal emotions to a rest of the world, thanks to the rapid growth of the Internet. Writing, photos, audio, and video are all used by many people to express their opinions or points of view. Every second, a massive amount of unstructured data are generated on the Internet as a consequence of social networking sites. To understand human psychology, data must be processed as soon as it is generated, which can be done via sentiment classification, which detects polarity in texts. It establishes if the writer has a negative, positive, or neutral attitude forward towards a specific item, administration, person, or region. Sentiment analysis is insufficient in some cases, needing emotion detection, which appropriately measures a person's emotional/mental state. This review investigates sentiment analysis levels, different emotion models, and sentiment analysis techniques such as emotion detection. Furthermore, the issues faced during sentiment or emotion analysis are addressed in this study. A range of machine learning techniques methods to assessing sentiment were also explored