International Journal of Science, Engineering and TechnologyAuthors: Keshvee Patel, Divya Shah, Ishan Tarkas, Prof. Medha Asurlekar
Abstract: Current systems for grievance redressal in cities are predominantly manually or semi-automated based which lead to delays, misclassification and slow handling of complaints. The work proposes LokSevaAI-a smart complaint management framework which automates complaint classification, prioritization and routing based on techniques from NLP and ML. Initially the unstructured text of the complaint is pre-processed. Then features from it are extracted using Term Frequency-Inverse Document Frequency. The features are then used to classify the complaint into a specific governance area using a supervised ML model like logistic regression, support vector machine or random forest for multi-class classification. A separate module for prioritization based on the sentiment analysis of the complaint has been implemented to attend to urgent and sensitive complaints first. Based on the classification and prioritization, automated routing has been enabled. A monitoring dashboard shows real-time status of complaints and helps in analysis and decision making. Efficient management of large datasets and storing of complaint data in a structured manner has also been taken into consideration for better monitoring throughout the life cycle of a complaint. Furthermore, it supports data-driven decision-making by providing features for analyzing trends and measuring performance. The proposed solution automates most of the tasks in the existing manual system and drastically improves response time and transparency. It is computationally inexpensive and feasible for deployment on a large scale in smart cities; there is also scope for integration with multi-lingual and voice-based services.
DOI: https://doi.org/10.5281/zenodo.20050331
