Authors : K. Meena; Dr. Girish Kumar D.; SreeLakshmi J.

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/3tnv4u2p

Scribd : https://tinyurl.com/muk9xzr8

DOI : https://doi.org/10.38124/ijisrt/26apr1987

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The contemporary digital landscape is defined by a massive proliferation of communication platforms, which has facilitated an unprecedented surge in user-generated textual content across e-commerce sites, social media, and online forums. This explosion of data presents a dual-edged sword for modern organizations: while it contains a wealth of subjective information reflecting individual opinions and evaluations of services, the sheer volume makes traditional evaluation methods obsolete. Manually analyzing this vast influx of unstructured data is inherently time-consuming, labor-intensive, and significantly prone to subjective interpretation or human bias. Consequently, there exists a critical need for automated systems that can efficiently bridge the gap between raw customer feedback and actionable business intelligence. This paper details the design and implementation of a robust, NLP-based customer sentiment analysis system engineered to automatically classify textual inputs into distinct positive, negative, or neutral sentiment categories. The proposed framework is built upon a modular architecture that integrates an input interface, a high-performance Natural Language Processing engine, a sentiment classification module, and a comprehensive visualization suite. By leveraging advanced text preprocessing techniques—including text normalization, to- ken generation, and the removal of irrelevant terms—the system successfully transforms noisy and informal textual data into a structured format suitable for high-accuracy computational analysis. A core feature of the system is its ability to handle both individual text entries and bulk processing through CSV file uploads, ensuring that it remains adaptable to varying organizational needs and data scales. Once the data is refined, the sentiment classification component utilizes specialized scoring techniques to assign polarity values based on the contextual and emotional characteristics of the text. These analyzed results are not merely discarded but are securely stored in a centralized results database to maintain historical records and support long-term trend evaluations. To enhance the interpretability of these findings for stakeholders, the system employs intuitive graphical analytics, such as bar and pie charts, which allow administrators to monitor sentiment distributions and iden- tify emerging issues at a glance. The framework is specifically designed to be scalable and interpretable, providing a userfriendly platform for non-technical users to extract meaningful insights from complex textual feedback. Furthermore, the current implementation establishes a strategic foundation for significant future enhancements, including the integration of transformer- based models for deeper linguistic understanding, multilingual support for global applicability, and real-time social media stream integration for proactive public opinion monitoring. Ultimately, the proposed solution provides organizations with a highly efficient tool for supporting data-driven strategies, improving customer satisfaction, and fostering long-term organizational growth through intelligent opinion mining.

Keywords : Sentiment Analysis, Natural Language Processing, Text Classification, Opinion Mining, Data Visualization, Customer Feedback Analysis.

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