National Language Processing For Sentiment Analysis in Social Media-A Comprehensive Review

Sentiment analysis (SA) is the computer study of end users' attitudes, opinions, and feelings around a certain subject or product. Sentiment analysis categorizes the message as favorable, negative, or neutral based on its polarity. Researchers have recently concentrated on sentiment analysis of social media posts using lexical and machine learning methods. Social media is a type of microblogging platform where users may leave comments in slang, which is made up of idioms, misspellings, symbols, and ironic statements. Social media data also suffer from the "curse of dimension," or the high dimensional character of the data necessitating certain extraction of features and preliminary processing processes to increase the precision of classification. Based on current research, this paper provides a thorough introduction of sentiment analysis approach. It then examines the methods of deep learning (DL) and machine learning (ML) in relation to sentiment analysis using social media data sets. Social media data are examined and pre-processed using the suggested framework, producing intersecting information on the strengths and weaknesses of sentiment analysis techniques.