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@article{203565,
        author = {sivanesh.R and kishothkumar.M and vasanth kumar.V and mariammal.R},
        title = {amazon review scraper and sentiment analyzer},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11161-11162},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203565},
        abstract = {The Amazon Review Scraper and Sentiment Analyzer is a comprehensive web-based application that automates the extraction and analysis of product reviews from Amazon's e-commerce platform. This system empowers users to make data-driven purchasing decisions by providing detailed sentiment insights derived from customer feedback. The application leverages cutting- edge technologies in both frontend and backend development, web scraping, and natural language processing to deliver actionable intelligence from consumer opinions. The backend utilizes Python, Flask, and Selenium for dynamic web scraping of Amazon's complex structure, while the Natural Language Processing (NLP) pipeline employs VADER Sentiment Analysis to classify reviews as positive, neutral, or negative. Meanwhile, the frontend interface built with HTML5, CSS3, and JavaScript provides an intuitive experience with responsive design, allowing users to visualize sentiment distribution through interactive charts and filter reviews based on specific criteria.},
        keywords = {},
        month = {May},
        }