Authors: Purushotam Naidu k, R.Srilatha, S.Gayathri, U.N. Harshitha, P. Siri Chandana
Abstract: Urban population growth creates new challenges related to civic infrastructure, and there is a need for efficient and smart complaint management systems. This paper describes the SocioSphere, which is an AI-based civic issue management platform that uses Natural Language Processing (NLP), machine learning, and high-performance web technologies to automatically process and route complaints. A report verification module (Fake/Real) built with Logistic Regression and engineered textual features can filter out spam and low-quality complaints. Valid complaints use a transformer model (RoBERTa) to identify the multi-class categories to which the complaint belongs. Furthermore, we have added a method of estimating the urgency of a complaint through the use of VADER-based sentiment analysis and heuristics for engagement, thus allowing for priority-based decisions. FastAPI is used to develop the backend API layer, offering high-speed (asynchronous/low latency) performance for model inferences and data processing. Complaints will be stored in the system's database and dynamically routed to appropriate authorities for final resolution. The experimental results demonstrate that the approach is effective for both classification and validation, as well as improving transparency and reducing manual work through the use of data-driven governance within smart city systems.
DOI: https://doi.org/10.5281/zenodo.20321890
