A REVIEW ARTICLE: USE OF SENTIMENT ANALYSIS IN SOCIAL MEDIA

Abstract

As a result of the growth of online social networking platforms and applications, a sizeable amount of user-generated text content is created daily in the form of comments, reviews, and short text messages. Users can write messages, share them, and add images and videos to social networking sites like Twitter, Facebook, and others. Consequently, a significant volume of sentiment-rich data is generated. Sentiment analysis then comes into play in this scenario, which evaluates opinions as positive, neutral, or negative by extracting, recognizing, or representing them from various sources, including social media, news, articles, and blogs. This study aims to analyze the results from different sentiment analysis models and technologies that combine natural language processing. Case studies of various industries that can benefit or have been benefiting from sentiment analysis are also discussed to provide an approachable pathway for anyone who wishes to go more deeply into this field. For example, the business world has used it to learn what customers think of a certain company or brand. The impact of profanity on how readers interpret tweets and other social media messages are studied in sociology and psychology. Political scientists are trying to anticipate election results based on tweets to evaluate answers, among other things, and to look for trends, ideological bias, and opinions. Researchers have previously evaluated numerous models using well-known techniques like Naive Bayes, support vector machines, etc., and the findings have been compared with promising outcomes.