Leveraging LLMs for Smart Cities Qualitative Data Analysis

Public authorities frequently conduct surveys and analyse data from citizens, a process that is often labour-intensive when performed manually. This paper explores how Generative Artificial Intelligence (GAI) can assist in automating data analysis for public authorities. In this respect, we investigate the potential of Large Language Models (LLMs) to perform sentiment analysis and summarisation of unstructured data as smart services. Using data from the East Bristol Liveable Neighbourhood (EBLN) as a case study, we assess the accuracy and precision of these models and validate the results against ground truth data and expert evaluations. Our findings indicate that sentiment classification achieved over 90% accuracy. In contrast, the expert validation rated the summarisation without context accuracy highly satisfactory, however, the satisfaction was low when contextual summarisation was evaluated. These results suggest that LLMs offer a promising approach to improving the efficiency of qualitative analysis, though further research is required to enhance their accuracy and usefulness.