Sentiment in Citizen Feedback: Exploration by Supervised Learning - Robin Lybeck; Samuel Rönnqvist; Sampo Ruoppila (Austria, 2022)
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 paper
reports 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.
Proceedings of the Proceedings of the EGOV-CeDEM-ePart 2018