NLP-based Sentiment Analysis on Twitter using Machine Learning Approaches

Internet services are generating a huge number of data that is increasing significantly every day. The exchange of ideas and information has exponentially risen with the usage of social media. Web users share more details with their friends and spend most time on social media, and the information obtained from their chat has been used in various applications. Social networking platforms, such as Twitter, Facebook, and Google+ are becoming increasingly popular, enabling users to share opinions on various topics, engage in discussions with diverse communities, and post messages globally. Sentiment Analysis (SA) is applied to Twitter data set for identifying the emotions of users and based on these, several issues can be solved. Twitter is a microblogging site that provides a good environment for SA. Sentiment classification done on Twitter data classifies the emotions in the tweets as neutral, negative, or positive. In this survey, 25 research papers on SA are reviewed. The methods of SA on Twitter are classified as Deep Learning, supervised methods, Hybrid, and other methods. For a reliable evaluation, research papers on SA published between 2015 and 2023 are collected. Finally, the evaluation is achieved based on the datasets, publication year, performance metrics, programming languages, and category analysis. Also, the programming language widely used for execution is Python.