Volume 2 - Issue 3, May - June 2026

Sentiment analysis of Twitter data has emerged as a powerful approach for understanding public opinion, trends, and user behavior in real time. This study focuses on leveraging Natural Language Processing (NLP) models to classify tweets into sentiment categories such as positive, negative, and neutral. Due to the informal and noisy nature of Twitter text—including abbreviations, emojis, hashtags, and slang—advanced preprocessing techniques such as tokenization, stop-word removal, stemming, and normalization are applied to improve data quality. Various machine learning and deep learning models, including Logistic Regression, Na¨ıve Bayes, Support Vector Machines, and transformer-based architectures, are employed to analyze sentiment patterns effectively.

Dr.A.Rajesh, B.Abhinash Goud, G.Ajay, B.Anil Kumar,"Sentiment Analysis Of Twitter Data Using NLP Models" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(3): Page(237-239) May-June 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.