Social media platforms (SMPs) have become a popular avenue worldwide for the general public to socialize. They exchange their views or experiences on various SMPs about any product or policies via social media posts. Considering that the SMPs being a rich source of data for understanding public opinion on a specific topic, this paper aims to present a classification model that can perform sentiment analysis using Twitter’s real-time data in the case of Citizenship Amendment Act (CAA). Public opinion on CAA is captured using Twitter API which is restricted to India. These tweets are processed using basic preprocessing techniques. Further, preprocessed data fed to various machine learning (ML) classification models for identifying the sentiment of the tweets. Each tweet is classified as positive, negative, and neutral based on its sentiment. The obtained results and analysis empowered us to understand Indian public opinion towards CAA over the period. A comparative analysis of various ML techniques is performed and Stochastic Gradient Descent has outperformed other ML techniques in terms of accuracy.