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@article{187669,
        author = {S.T.Preethi and K.Vani},
        title = {Universal Product Intelligence Engine: A Hybrid NLP and Web-Mining Framework for Dynamic Product Reputation Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {6786-6797},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187669},
        abstract = {In today’s data-driven economy, the perception of a product’s quality and value is no longer shaped solely by brand reputation or advertising, but by the vast and ever-evolving landscape of online consumer feedback. Millions of users express opinions about products daily across diverse digital platforms—ranging from e-commerce sites and technical forums to blogs, social networks, and video review portals. The challenge, however, lies in aggregating, interpreting, and synthesizing these distributed voices into coherent, actionable insights that businesses can rely upon. Conventional systems depend on rigid, single-source scraping or narrowly trained sentiment models, both of which fail to capture the diversity, context, and dynamism of real-world opinions.
This research presents Universal Product Intelligence Engine 2.0, an advanced hybrid framework that redefines large-scale opinion mining by merging explainable rule-based methods with modern deep learning architectures. Unlike prior models restricted to specific platforms, this system performs generalized web discovery, dynamically identifying and collecting relevant product-related feedback from the open internet. It employs a multi-layered pipeline encompassing data acquisition, linguistic preprocessing, hybrid sentiment computation, topic modelling, transformer-based summarization, and regex-driven competitor detection.},
        keywords = {Natural Language Processing (NLP); Hybrid Sentiment Analysis; Topic Modelling; Web Scraping; Transformer Models; Product Intelligence; T5 Summarization; Explainable AI; Competitive Mining; Multi-Source Opinion Aggregation; Web-Scale Data Analytics.},
        month = {November},
        }