Proceedings of the Proceedings of the EGOV-CeDEM-ePart 2018

Abstract: Web-based citizen feedback systems have become commonplace in cities around the world, resulting in vast amounts of data. Recent advances in machine learning and natural language processing enable novel and practical ways of analysing it as big data. This paperreports an explorative case study of sentiment analysis of citizen feedback (in Finnish) by means of annotation with custom categories (Positive, Neutral, Negative, Angry, Constructive and Unsafe) and predictive modelling. We analyse the results quantitatively and qualitatively, illustrate the benefits of such an approach, and discuss the use of machine learning in the context of studying citizen feedback. Custom annotation is a laborious process, but it offers task-specific adaptation and enables empirically grounded analysis. In this study, annotation was carried out at a moderate scale. The resulting model performed well in the most frequent categories, while the infrequent ones remained a challenge. Nonetheless, this kind of approach has promising features for developing automated systems of processing textual citizen feedback.