A4 Article in conference proceedings
Sentiment in Citizen Feedback: Exploration by Supervised Learning




List of Authors: Robin Lybeck, Samuel Rönnqvist, Sampo Ruoppila
Place: Krems
Publication year: 2018
Book title *: Proceedings of the Proceedings of the EGOV-CeDEM-ePart 2018
ISBN: 978-3-903150-22-5
ISSN: 2524-1400

Abstract

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.





Downloadable publication

This is an electronic reprint of the original article.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.




Last updated on 2019-29-01 at 11:13