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{187517,
        author = {K.Vani and C.Ashwitha},
        title = {Smart Civic Complaint Analyzer Using Natural Language Processing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {5408-5415},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187517},
        abstract = {The Smart Civic Complaint Analyzer is an AI-enabled web interface designed to enable local citizens to find and report on city problems easier while city officials can be faster to respond. The platform incorporates Natural Language Processing (NLP) to identify complaints made in simple text by a person. The system can automatically categorize the type of complaint (Water, Roads, Electricity, or Sanitation), identify how urgent the complaint is (High, Medium, or Low) and identify the location. Local people can easily report a complaint or can upload multiple complaints in a compatible CSV format. Users can see all complaints submitted to the system, search and filter complaints to manage and track their reports and complaints. City officials can use an interactive dashboard to view the information submitted through visual charts and word clouds live and have summaries of the data to assist them to measure the most common, frequent and serious complaints in an area. The application is developed in Python and uses Streamlit to build the User Interface (UI) application. The text analysis used machine learning models including TF-IDF and Naive Bayes. The project stores data as an excel workbook file, to ensure while data is easy to access, the data can also be exported in a similar format. Municipalities can use this system to save time, better plan-prevention, and increase transparency with the public, while the public has a quicker and more open way to report on local problems. Overall, this project is designed to make smart cities smarter, faster, and more citizen friendly.},
        keywords = {Natural Language Processing, Machine Learning, TF-IDF, Naive Bayes, Complaint Classification, Urgency Detection, Location Extraction, Word Cloud, Citizen Engagement, Data Visualization, Municipal Management, Smart City Solutions.},
        month = {November},
        }