國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Extracting knowledge from opinion mining

Agrawal, Rashmi,

Extracting knowledge from opinion mining

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Extracting knowledge from opinion miningRashmi Agrawal and Neha Gupta, editors.
其他作者:	Agrawal, Rashmi,
出版者:	Hershey, Pennsylvania :IGI Global,[2019]
面頁冊數:	1 online resource (xxvii, 346 p.)
標題:	Data mining.
電子資源:	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:

184440
Data mining.

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

Dewey Class. No.: 006.3/12

Extracting knowledge from opinion mining
LDR:02407nmm a2200277 a 4500 001 560139
003 IGIG
005 20191023161810.0
006 m o d
007 cr cn
008 200109s2018 pau fob 001 0 eng d
010 $z 2018001725
020 $a 9781522561187 (e-book)
020 $a 9781522561170 (hardback)
035 $a (OCoLC)1048608875
035 $a 1081021285
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 184440
650 0 $a Discourse analysis $x Data processing. $3 218456
650 0 $a Language and emotions. $3 299158
650 0 $a Public opinion. $3 187693
700 1 $a Agrawal, Rashmi, $e editor. $3 843777
700 1 $a Gupta, Neha, $e editor. $3 843778
856 4 0 $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-6117-0