J. Romilda Antony, S. Preethi, P. Sireesha, Ms. M. Benita Roy
Abstract
This journal version expands upon the original IEEE-style paper by providing a more comprehensive explanation of the complaint management framework, including data collection, preprocessing, prioritization techniques, and system implementation. The study focuses on analyzing complaint text data submitted by citizens in order to identify the urgency and nature of the issues reported. Advanced Natural Language Processing (NLP) techniques are applied to process and interpret unstructured complaint data effectively. In particular, Named Entity Recognition (NER) is used to extract key entities such as locations, organizations, and issue-related terms from the complaint text. These extracted entities help the system understand the context of each complaint and support accurate prioritization. By combining text analysis with prioritization strategies, the proposed system enables faster identification of critical complaints and assists organizations in responding more efficiently, ultimately improving the overall effectiveness of citizen complaint management systems.
Keywords
Complaint Management, Text Mining, Named Entity Recognition, NLP, Data Analytics
Conclusion
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How to Cite This Paper
J. Romilda Antony, S. Preethi, P. Sireesha, Ms. M. Benita Roy (2026). GREEN CREDIT ENVIRONMENT TRACKING SYSTEM. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
