A Qualified Study of Machine Learning Algorithms for Sentiment Analysis in Social Media
Shyelendra Madansing Pardeshi, Dinesh Chandra Jain, R. B. Wagh · 2023
Social media has become an essential part of our everyday lives as a result of the widespread use of the Internet. Among the most widely used social media sites right now is Twitter. The era of the internet is now. Online forums, blog postings, tweets, and other forms of communication are used by people. The quantity of data created is significant as a consequence of increasing social networking. This material is a great resource for learning about various aspects of life, including business, marketing, trend analysis, and forecasting, among others. This paper discusses several machine learning sentiment analysis techniques. Different machine learning classifiers, such as Naive Bayes, Random Forest, Support Vector Machine, and KNN, were used to evaluate sentiment. The practice of automatically categorizing user-generated information as positive, negative, or neutral is known as sentiment analysis. The text, sentence, feature, or aspect may all be used as criteria for categorizing feelings into different classifications. This research shows how to analyze emotions expressed on the Twitter network using machine learning techniques. To have a better understanding, different machine learning approaches are compared.