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Extracting knowledge from opinion mining

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Extracting knowledge from opinion mining/ Rashmi Agrawal and Neha Gupta, editors.
other author:	Agrawal, Rashmi,
Published:	Hershey, Pennsylvania :IGI Global, : [2019],
Description:	1 online resource (xxvii, 346 p.)
Subject:	Data mining. -
Online resource:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-6117-0
ISBN:	9781522561187 (e-book)

Extracting knowledge from opinion mining
Extracting knowledge from opinion mining[electronic resource] /Rashmi Agrawal and Neha Gupta, editors. - Hershey, Pennsylvania :IGI Global,[2019] - 1 online resource (xxvii, 346 p.)

Includes bibliographical references and index.

Section 1. Introductory concepts of opinion mining. Chapter 1. Fundamentals of opinion mining ; Chapter 2. Feature based opinion mining ; Chapter 3. Deep learning for opinion mining ; Chapter 4. Opinion mining: using machine learning techniques -- Section 2. Ontologies and their applications. Chapter 5. Ontology-based opinion mining ; Chapter 6. Ontologies, repository, and information mining in component-based software engineering environment ; Chapter 7. Ontology-based opinion mining for online product reviews ; Chapter 8. Applications of ontology-based opinion mining -- Section 3. Tools and techniques of opinion mining. Chapter 9. Tools of opinion mining ; Chapter 10. Sentimental analysis tools ; Chapter 11. Anatomizing lexicon with natural language Tokenizer Toolkit 3 -- Section 4. Challenges and open issues of opinion mining. Chapter 12. Challenges of text analytics in opinion mining ; Chapter 13. Open issues in opinion mining -- Section 5. Case study. Chapter 14. Case study: efficient faculty recruitment using genetic algorithm

Restricted to subscribers or individual electronic text purchasers.

"This book covers the key topics of opinion mining and sentiment analysis. It includes future trends and research directions related to opinion supervised and unsupervised approaches for opinion mining, machine learning techniques, deep learning and opinion spam detection. The book also includes some open source tools for opinion mining and sentiment analysis"--Provided by publisher.

ISBN: 9781522561187 (e-book)Subjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343 / E9984 2019e

Dewey Class. No.: 006.3/12

Extracting knowledge from opinion mining
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020 $a 9781522561187 (e-book)
020 $a 9781522561170 (hardback)
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040 $a CaBNVSL $b eng $c CaBNVSL $d CaBNVSL
050 0 0 $a QA76.9.D343 $b E9984 2019e
082 0 0 $a 006.3/12 $2 23
245 0 0 $a Extracting knowledge from opinion mining $h [electronic resource] / $c Rashmi Agrawal and Neha Gupta, editors.
260 $a Hershey, Pennsylvania : $b IGI Global, $c [2019]
300 $a 1 online resource (xxvii, 346 p.)
504 $a Includes bibliographical references and index.
505 0 $a Section 1. Introductory concepts of opinion mining. Chapter 1. Fundamentals of opinion mining ; Chapter 2. Feature based opinion mining ; Chapter 3. Deep learning for opinion mining ; Chapter 4. Opinion mining: using machine learning techniques -- Section 2. Ontologies and their applications. Chapter 5. Ontology-based opinion mining ; Chapter 6. Ontologies, repository, and information mining in component-based software engineering environment ; Chapter 7. Ontology-based opinion mining for online product reviews ; Chapter 8. Applications of ontology-based opinion mining -- Section 3. Tools and techniques of opinion mining. Chapter 9. Tools of opinion mining ; Chapter 10. Sentimental analysis tools ; Chapter 11. Anatomizing lexicon with natural language Tokenizer Toolkit 3 -- Section 4. Challenges and open issues of opinion mining. Chapter 12. Challenges of text analytics in opinion mining ; Chapter 13. Open issues in opinion mining -- Section 5. Case study. Chapter 14. Case study: efficient faculty recruitment using genetic algorithm
506 $a Restricted to subscribers or individual electronic text purchasers.
520 3 $a "This book covers the key topics of opinion mining and sentiment analysis. It includes future trends and research directions related to opinion supervised and unsupervised approaches for opinion mining, machine learning techniques, deep learning and opinion spam detection. The book also includes some open source tools for opinion mining and sentiment analysis"--Provided by publisher.
650 0 $a Data mining. $3 528622
650 0 $a Discourse analysis $x Data processing. $3 561677
650 0 $a Language and emotions. $3 574732
650 0 $a Public opinion. $3 593813
700 1 $a Agrawal, Rashmi, $e editor. $3 1235034
700 1 $a Gupta, Neha, $e editor. $3 1235035
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-6117-0