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@article{200400,
        author = {Jayashree Janani Murugesan and Devadharshini S and Loganarayanan R},
        title = {CUSTOMER REVIEW SENTIMENT CLASSIFICATION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {605-608},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200400},
        abstract = {Customer review sentiment classification is a major application of Natural Language Processing used to identify customer opinions, emotions, and satisfaction levels from textual feedback. Modern businesses receive large numbers of reviews through e-commerce websites, food delivery applications, hotel booking portals, and social media platforms. Manual analysis of such large review datasets is difficult, slow, and inconsistent. This project develops a Python-based machine learning system to automatically classify customer reviews into positive, negative, and neutral sentiments. Text preprocessing methods such as tokenization, stop-word removal, punctuation removal, lowercasing, stemming, lemmatization, and TF-IDF vectorization are applied to improve prediction accuracy. The Multinomial Naive Bayes algorithm is used for classification because it is simple, fast, memory-efficient, and highly effective for text classification tasks. The system improves customer satisfaction analysis, service quality, and business decision-making.},
        keywords = {Sentiment Analysis, Machine Learning, Natural Language Processing, Python, Customer Reviews, TF-IDF, Naive Bayes},
        month = {May},
        }