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From text to understanding = using fuzzy sets to analyse free-form text data /

Record Type:	Electronic resources : Monograph/item
Title/Author:	From text to understanding/ by Miloš Švaňa, František Zapletal, Miroslav Hudec.
Reminder of title:	using fuzzy sets to analyse free-form text data /
Author:	Švaňa, Miloš.
other author:	Zapletal, František.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xix, 112 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Text data mining. -
Online resource:	https://doi.org/10.1007/978-3-032-00129-0
ISBN:	9783032001290

From text to understanding = using 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

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

3495453
Text data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

From text to understanding = using fuzzy sets to analyse free-form text data /
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505 0 $a 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.
520 $a 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.
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