Utilizing NLP to Optimize Municipal Services Delivery Using a Novel Municipal Arabic Dataset

Abstract

The natural language processing paradigm has emerged as a vital tool for addressing complex business challenges, mainly due to advancements in machine learning (ML), deep learning (DL), and Generative AI. Advanced NLP models have significantly enhanced the efficiency and effectiveness of NLP applications, enabling the seamless integration of various business processes to improve decision-making. In the municipal sector, the Kingdom of Saudi Arabia is trying to harness the power of NLP to promote urban development, city planning, and infrastructure enhancements, ultimately elevating the quality of life for its residents. In the municipal sector, approximately 300 services are available through multiple channels, including the Baladi application, unified communication services, WhatsApp, a dedicated beneficiary center (serving citizens and residents), and social media accounts. These channels are supported by a dedicated team that operates 24/7. This paper examines the implementation of ML and DL methods to categorize requests and suggestions submitted by residents for various municipal services in the Kingdom of Saudi Arabia. The primary aim of this work is to enhance service quality and reduce response times to community inquiries. However, a significant challenge arises from the lack of Arabic datasets specifically tailored to the municipal sector for training purposes, which limits meaningful progress. To address this issue, we have created a novel dataset consisting of 3,714 manually classified requests and suggestions collected from the X platform. This dataset is organized into eight classes: tree maintenance, lighting, construction waste, old and neglected assets, road conditions, visual pollution, billboards, and cleanliness. Our findings indicate that ML models, particularly when optimized with hyperparameters and appropriate pre-processing, outperformed DL models, achieving an F1 score of 90% compared to 88%. By releasing this novel Arabic dataset, which will be open sourced for the scientific community, we believe this work provides a foundational reference for further research and significantly contributes to improving the municipal sector's service delivery.