Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{194866,
        author = {V.Siva Shankar and J.Madhu Teja and D.Siva RamaKrishna and D.Harshitha and G.Bhagya Harshika Vardhini},
        title = {FLIPKART PRODUCT REVIEW ANALYSIS FOR CUSTOMER SATISFACTION INSIGHTS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {8101-8107},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194866},
        abstract = {In today’s e-commerce environment, customer reviews play an important role in helping people make purchasing decisions and understand product quality, especially on platforms like Flipkart where thousands of reviews are posted daily. Manually analyzing such large amounts of feedback is difficult, so this project focuses on developing a machine learning system that automatically classifies reviews into positive, negative, and neutral sentiments. The study uses a publicly available dataset of product reviews, which is processed using techniques like text cleaning, tokenization, stop-word removal, and vectorization. Algorithms such as Logistic Regression, Decision Tree, and K-Nearest Neighbors are applied for sentiment classification, and the results show that machine learning can effectively identify customer opinions and provide useful insights to help businesses understand customer satisfaction and improve their products.},
        keywords = {Sentiment Analysis, Flipkart Reviews, Machine Learning, Natural Language Processing (NLP), Text Classification, Customer Satisfaction, Data Preprocessing, Logistic Regression, naïve bayes, Random-forest.},
        month = {March},
        }