Extracting knowledge from opinion mining:
"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 include...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) :
IGI Global,
[2019]
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "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"-- |
| Beschreibung: | 28 PDFs (xxvii, 346 pages) Also available in print. |
| Format: | Mode of access: World Wide Web. |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781522561187 |
| Zugangseinschränkungen: | Restricted to subscribers or individual electronic text purchasers. |
Internformat
MARC
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| 020 | |z 9781522561170 |q hardcover | ||
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| 040 | |a CaBNVSL |b eng |e rda |c CaBNVSL |d CaBNVSL | ||
| 050 | 4 | |a QA76.9.D343 |b E9984 2019e | |
| 082 | 7 | |a 006.3/12 |2 23 | |
| 245 | 0 | 0 | |a Extracting knowledge from opinion mining |c Rashmi Agrawal and Neha Gupta, editors. |
| 264 | 1 | |a Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) : |b IGI Global, |c [2019] | |
| 300 | |a 28 PDFs (xxvii, 346 pages) | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a electronic |2 isbdmedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 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"-- |c Provided by publisher. | |
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 588 | |a Description based on title screen (IGI Global, viewed 08/14/2018). | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Discourse analysis |x Data processing. | |
| 650 | 0 | |a Language and emotions. | |
| 650 | 0 | |a Public opinion. | |
| 700 | 1 | |a Agrawal, Rashmi, |e editor. | |
| 700 | 1 | |a Gupta, Neha, |e editor. | |
| 710 | 2 | |a IGI Global, |e publisher. | |
| 776 | 0 | |c (Original) |w (DLC)2018001725 | |
| 776 | 0 | 8 | |i Print version: |z 152256117X |z 9781522561170 |w (DLC) 2018001725 |
| 856 | 4 | 0 | |l FWS01 |p ZDB-98-IGB |q FWS_PDA_IGB |u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-6117-0 |3 Volltext |
| 912 | |a ZDB-98-IGB | ||
| 049 | |a DE-863 | ||
Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-98-IGB-00197078 |
|---|---|
| _version_ | 1816797080433197056 |
| adam_text | |
| any_adam_object | |
| author2 | Agrawal, Rashmi Gupta, Neha |
| author2_role | edt edt |
| author2_variant | r a ra n g ng |
| author_facet | Agrawal, Rashmi Gupta, Neha |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
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| collection | ZDB-98-IGB |
| contents | 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 |
| ctrlnum | (CaBNVSL)slc20371622 (OCoLC)1048608875 |
| dewey-full | 006.3/12 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/12 |
| dewey-search | 006.3/12 |
| dewey-sort | 16.3 212 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | ZDB-98-IGB-00197078 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-26T14:51:53Z |
| institution | BVB |
| isbn | 9781522561187 |
| language | English |
| oclc_num | 1048608875 |
| open_access_boolean | |
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| owner_facet | DE-863 DE-BY-FWS |
| physical | 28 PDFs (xxvii, 346 pages) Also available in print. |
| psigel | ZDB-98-IGB |
| publishDate | 2019 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | IGI Global, |
| record_format | marc |
| spelling | Extracting knowledge from opinion mining Rashmi Agrawal and Neha Gupta, editors. Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) : IGI Global, [2019] 28 PDFs (xxvii, 346 pages) text rdacontent electronic isbdmedia online resource rdacarrier 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. Also available in print. Mode of access: World Wide Web. Description based on title screen (IGI Global, viewed 08/14/2018). Data mining. Discourse analysis Data processing. Language and emotions. Public opinion. Agrawal, Rashmi, editor. Gupta, Neha, editor. IGI Global, publisher. (Original) (DLC)2018001725 Print version: 152256117X 9781522561170 (DLC) 2018001725 FWS01 ZDB-98-IGB FWS_PDA_IGB http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-6117-0 Volltext |
| spellingShingle | Extracting knowledge from opinion mining 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 Data mining. Discourse analysis Data processing. Language and emotions. Public opinion. |
| title | Extracting knowledge from opinion mining |
| title_auth | Extracting knowledge from opinion mining |
| title_exact_search | Extracting knowledge from opinion mining |
| title_full | Extracting knowledge from opinion mining Rashmi Agrawal and Neha Gupta, editors. |
| title_fullStr | Extracting knowledge from opinion mining Rashmi Agrawal and Neha Gupta, editors. |
| title_full_unstemmed | Extracting knowledge from opinion mining Rashmi Agrawal and Neha Gupta, editors. |
| title_short | Extracting knowledge from opinion mining |
| title_sort | extracting knowledge from opinion mining |
| topic | Data mining. Discourse analysis Data processing. Language and emotions. Public opinion. |
| topic_facet | Data mining. Discourse analysis Data processing. Language and emotions. Public opinion. |
| url | http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-6117-0 |
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