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From text to understandingusing fuzzy sets to analyse free-form text data /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	From text to understandingby Miloš Švaňa, František Zapletal, Miroslav Hudec.
其他題名:	using fuzzy sets to analyse free-form text data /
作者:	Švaňa, Miloš.
其他作者:	Zapletal, František.
出版者:	Cham :Springer Nature Switzerland :2025.
面頁冊數:	xix, 112 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Text data mining.
電子資源:	https://doi.org/10.1007/978-3-032-00129-0
ISBN:	9783032001290$q(electronic bk.)

From text to understandingusing fuzzy sets to analyse free-form text data /
Švaňa, Miloš.

From text to understandingusing fuzzy sets to analyse free-form text data /[electronic resource] :by Miloš Švaňa, František Zapletal, Miroslav Hudec. - Cham :Springer Nature Switzerland :2025. - xix, 112 p. :ill., digital ;24 cm. - Fuzzy management methods,2196-4149. - Fuzzy management methods..

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.

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.

ISBN: 9783032001290$q(electronic bk.)

Standard No.: 10.1007/978-3-032-00129-0doiSubjects--Topical Terms:

892173
Text data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

From text to understandingusing fuzzy sets to analyse free-form text data /
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