Social media and other sources of text data are still underutilized resources in public decision-making. Most public organizations and governmental bodies rely mainly only surveys, interviews and other traditional methods for gathering opinions. One issues is a lack of easy-to-use tools for mass text data processing that would enable these organizations to process and understand this type of data.

This book introduces a novel text data analysis framework designed for public decision making, specifically on the level of municipalities. The framework combines sentiment analysis with topic modelling and a fuzzy-based approach for capturing the diversity in sentiment arising from the fact that different people have different opinions on a given topic.

The book is recommended for practitioners in public decision making as well as researchers analyzing large amounts of text data in order to understand people's opinions.

Presents a unique combination of fuzzy approaches and NLP methods Demonstrates how to capture the diversity of opinions and make effective use of the information obtained Illustrates how fuzzy and NLP methods can be used to support public decision making

Autorentext

Miloš Šväa is a researcher at VSB – Technical University of Ostrava, Czechia. He specializes in dealing with different forms of uncertainty in decision making, employing methods from fields such as natural language processing, fuzzy set theory, or machine learning. He is also a hobbyist software developer and occasional blogger. František Zapletal is a full professor of systems engineering and informatics, affiliated to Department of Systems Engineering and Informatics at VŠB – Technical university of Ostrava, Faculty of Economics, Czech Republic (Head of the department since 2024). His main areas of interest are multi-criteria decision-making methods and optimization methods especially under risk and uncertainty. He took a part in two long-term research internships: KCGI Kyoto (Japan, 8 months, 2015) and University of Granada (Spain, 6 months, 2018-2019). He also provided several invited speeches on stochastic and fuzzy decision-making (KCGI Kyoto, Japan; Tampere, Finland; Lappeenranta, Finland; Stavanger, Norway; Katowice, Poland). Since 2012, he has participated in 11 research projects, published more than 20 papers in international research journals, 2 workbooks and 1 monograph, presented over 40 contributions in conference proceedings, and provided his expertise for 7 top indexed international journals focused on operations research indexed in Web of Science database. He is also a head of the programme committee of Strategic Management and its Support by Information Systems international conference indexed in Scopus. In 2021, he has been elected as a member of the board of the Czech Society for Operations Research. Since 2022, he has worked as an editor of Applied Soft Computing journal. Miroslav Hudec is an associate professor at the University of Economics in Bratislava (Slovakia) and at the VSB – Technical University of Ostrava (Czech Republic). Currently, he is a visiting professor at the University of Belgrade (Serbia). Since 2013, Miroslav has been working in academia. During this period, he published approximately 30 publications, of which eight articles in WoS Q1 journals and a monograph in Springer, common publications with students from several countries, and gave five invited talks (in Europe, Canada, and Japan). His research activities are evolving around soft computing (mainly fuzzy logic), business intelligence, aggregation functions, and decision support systems. Currently, he serves as an associate editor in the journals Applied Soft Computing (WoS Q1) and International Journal of Interactive Multimedia and Artificial Intelligence (WoS Q2). In years 2005-2009, Miroslav was a representative of the Slovak Republic on the Meetings on Management of Statistical Information Systems organised by UNECE. At the IPMU 2022 conference in Milan, his co-authored work received the Lotfi Zadeh Best Paper Award for solving problems in flexible mixed aggregation functions.

Inhalt

Chapter 1. Citizen Engagement in Smart Cities Municipal Decision-Making.- Part I: Methodology.- Chapter 2. Fuzzy Sets and Fuzzy Algebra.- Chapter 3. Machine learning and Natural Language Processing.- Part II: Framework design and numerical experiments.- Chapter 4. Framework design.- Chapter 5. Numerical experiments.- Part III: Conclusions and recommendations.- Chapter 6. Discussion and Recommendations.- Chapter 7. Conclusions.