Improving municipal decision-making with topic modeling and sentiment analysis

Abstract Social networks are a great source of data for municipal decision making, as they allow citizens to freely express their opinions. But due to their unstructured nature and large volume, these data are difficult to process. Therefore, we propose a social network data analysis framework for municipal decision making consisting of four main steps: (1) topic modeling, (2) sentiment analysis, (3) combining the results of the previous two steps into a triangular fuzzy number, which captures sentiment diversity, and (4) calculating the levels of positive and negative sentiment. For step (3) we propose a method of constructing a triangular fuzzy number by calculating weighted mean sentiment and its weighted “standard semi-deviation”, and for step (4) we propose using degree of similarity with prototypical fuzzy sets representing the concepts of positive and negative sentiment. The most important practical innovation of the framework is its ability do capture not only some sort of an average sentiment towards a topic, but also its diversity. The framework was evaluated on about 30,000 tweets published from Ostrava, Czechia, over a period of 5 months. We demonstrate how analyzing sentiment diversity can be useful for municipal decision makers by extracting several practical recommendations from the results provided by the framework. We also show that the framework provides more information than a naive approach deployed by many commercial tools which fails to properly distinguish between mean sentiment towards a topic and its diversity.