A Review: Sentiment Analysis Methods and their use in Social Media Platforms

The method for recognizing positive, neutrality, or negativity sentiment in text is known as sentiment analysis. Sentimental analysis is mostly helpful for enhancing customer service, promoting goods and services, anticipating opinion polls, enhancing awareness of the signification of data security, and identifying how spectators feel about a certain sport. This paper examines the methodology employed in a review of sentimental analysis across several platforms, with a focus on social media, and discusses its applications. Social media networks have a lot of unprocessed data that users post as text and video. The utilization of sentimental analysis can transform the data into useful knowledge. The following reliable sources, including IEEE Xplore, Science Direct, and Springer, were utilized to carry out a systematic review of studies from 2016. 23 of the 50 articles that were originally and thoroughly reviewed have been chosen for the review process. The results demonstrate that most articles used lexicon- and machine-learning-based approaches to analyze text sentiment on social media platforms, with applications in industry, medical services, and safety.