Sentiment analysis in social networks / (Record no. 247447)
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| control field | ocn960458243 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OCoLC |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20190328114817.0 |
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| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu---unuuu |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 161012s2017 mau ob 001 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | N$T |
| Language of cataloging | eng |
| Description conventions | rda |
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| 019 ## - | |
| -- | 961139135 |
| -- | 962390352 |
| -- | 962434519 |
| -- | 962786861 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780128044384 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 0128044381 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9780128044124 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 0128044128 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)960458243 |
| Canceled/invalid control number | (OCoLC)961139135 |
| -- | (OCoLC)962390352 |
| -- | (OCoLC)962434519 |
| -- | (OCoLC)962786861 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA76.9.N38 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM x 042000 |
| Source | bisacsh |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3/12 |
| Edition number | 23 |
| 245 00 - TITLE STATEMENT | |
| Title | Sentiment analysis in social networks / |
| Medium | [electronic resource] |
| Statement of responsibility, etc. | edited by Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Cambridge, MA : |
| Name of producer, publisher, distributor, manufacturer | Morgan Kaufmann, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2017. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | �2017 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references and index. |
| 588 0# - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Online resource, title from PDF title page (EBSCO, viewed October 15, 2016). |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologiesProvides insights into opinion spamming, reasoning, and social network analysisShows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequencesServes as a one-stop reference for the state-of-the-art in social media analytics. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Machine generated contents note: ch. 1 Challenges of Sentiment Analysis in Social Networks: An Overview -- 1. Background -- 2. Sentiment Analysis in Social Networks: A New Research Approach -- 3. Sentiment Analysis Characteristics -- 3.1. Sentiment Categorization: Objective Versus Subjective Sentences -- 3.2. Levels of Analysis -- 3.3. Regular Versus Comparative Opinion -- 3.4. Explicit Versus Implicit Opinions -- 3.5. The Role of Semantics -- 3.6. Dealing with Figures of Speech -- 3.7. Relationships in Social Networks -- 4. Applications -- References -- ch. 2 Beyond Sentiment: How Social Network Analytics Can Enhance Opinion Mining and Sentiment Analysis -- 1. Introduction -- 2. Definitions and History of Online Social Networks -- 2.1. What Exactly Is an Online Social Network? -- 2.2. Brief History of Online Social Networks -- 3. Are Online Social Networks All the Same? Features and Metrics -- 3.1. Types of User-Generated Content -- 3.2. Types of Relationships Between Users. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Note continued: 3.3. Indexes and Metrics to Analyze Data Collected Through Online Social Networks -- 4. Psychological and Motivational Factors for People to Share Opinions and to Express Themselves on Social Networks -- 4.1. Need to Belong -- 4.2. Need for Cognition -- 4.3. Self-Presentation and Impression Management -- 5. From Sociology Principles to Social Networks Analytics -- 5.1. Tie Strengths -- 5.2. Homophily or Similarity Breeds Connection -- 5.3. Source Credibility -- 6. How Can Social Network Analytics Improve Sentiment Analysis on Online Social Networks? -- 6.1. What Is Social Network Analysis? -- 6.2. How to Integrate Social Network Analytics in Sentiment Analysis: Some Examples -- 7. Conclusion and Future Directions -- References -- ch. 3 Semantic Aspects in Sentiment Analysis -- 1. Introduction -- 2. Semantic Resources for Sentiment Analysis -- 2.1. Classical Resources on Sentiment. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Note continued: 2.2. Beyond the Polarity Valence: Emotion Lexica, Ontologies, and Psycholinguistic Resources -- 2.3. Social Media Corpora Annotated for Sentiment and Fine Emotion Categories -- 3. Using Semantics in Sentiment Analysis -- 3.1. Lexical Information -- 3.2. Distributional Semantics -- 3.3. Entities, Properties, and Relations -- 3.4. Concept-Level Sentiment Analysis: Reasoning with Semantics -- 4. Conclusions -- ch. 4 Linked Data Models for Sentiment and Emotion Analysis in Social Networks -- 1. Introduction -- 2. Marl: A Vocabulary for Sentiment Annotation -- 3. Onyx: A Vocabulary for Emotion Annotation -- 3.1. Onyx Extensibility: Vocabularies -- 3.2. Emotion Markup Language -- 4. Linked Data Corpus Creation for Sentiment Analysis -- 4.1. Sentiment Corpus -- 4.2. Emotion Corpus -- 5. Linked Data Lexicon Creation for Sentiment Analysis -- 5.1. Sentiment Lexicon -- 5.2. Emotion Lexicon -- 6. Sentiment and Emotion Analysis Services. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Note continued: 7. Case Study: Generation of a Domain-Specific Sentiment Lexicon -- 8. Conclusions -- Acknowledgments -- References -- ch. 5 Sentic Computing for Social Network Analysis -- 1. Introduction -- 2. Related Work -- 3. Affective Characterization -- 4. Applications -- 4.1. Troll Filtering -- 4.2. Social Media Marketing -- 4.3.A Model for Sentiment Classification in Twitter -- 5. Future Trends and Directions -- 6. Conclusion -- References -- ch. 6 Sentiment Analysis in Social Networks: A Machine Learning Perspective -- 1. Introduction -- 2. Polarity Classification in Online Social Networks: The Key Elements -- 3. Polarity Classification: Natural Language and Relationships -- 3.1. Leveraging Natural Language -- 3.2. Leveraging Natural Language and Relationships -- 4. Applications -- 5. Future Directions -- 6. Conclusion -- References -- ch. 7 Irony, Sarcasm, and Sentiment Analysis -- 1. Introduction -- 2. Irony and Sarcasm Detection -- 2.1. Irony Detection -- 2.2. Sarcasm Detection. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Note continued: 3. Figurative Language and Sentiment Analysis -- 3.1. Sentiment Polarity Classification at Evalita 2014 -- 3.2. Sentiment Analysis in Twitter at SemEval 2014 and 2015 -- 3.3. Sentiment Analysis of Figurative Language in Twitter at SemEval 2015 -- 4. Future Trends and Directions -- 5. Conclusions -- Acknowledgments -- References -- ch. 8 Suggestion Mining From Opinionated Text -- 1. Introduction -- 2. Sentiments and Suggestions -- 3. Task Definition and Typology of Suggestions -- 4. Datasets -- 5. Approaches for Suggestion Detection -- 5.1. Linguistic Observations in Suggestions -- 5.2. Detection of Suggestions for Improvements -- 5.3. Detection of Suggestions to Fellow Customers -- 6. Applications -- 7. Future Trends and Directions -- 8. Summary -- Acknowledgments -- References -- ch. 9 Opinion Spam Detection in Social Networks -- 1. Introduction -- 2. Related Work -- 3. Review Spammer Detection Leveraging Reviewing Burstiness -- 3.1. Burst Detection. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Note continued: 3.2. Spammer Detection Under Review Bursts -- 4. Detecting Campaign Promoters on Twitter -- 4.1. Campaign Promoter Modeling Using Typed Markov Random Fields -- 4.2. Inference -- 5. Spotting Spammers Using Collective Positive-Unlabeled Learning -- 5.1. Problem Definition -- 5.2. Collective Classification -- 5.3. Model Evaluation -- 5.4. Trends and Directions -- 6. Conclusion -- Acknowledgments -- References -- ch. 10 Opinion Leader Detection -- 1. Introduction -- 2. Problem Definition -- 3. Approaches -- 3.1. Measures Based on Network Structure -- 3.2. Methods Based on Interaction -- 3.3. Methods Based on Content Mining -- 3.4. Methods Based on Content and Interaction -- 4. Discussion -- 5. Conclusions -- References -- ch. 11 Opinion Summarization and Visualization -- 1. Introduction -- 2. Opinion Summarization -- 2.1. Challenges -- 2.2. Evaluation -- 2.3. Opinion Summarization Approaches -- 3. Opinion Visualization -- 3.1. Challenges for Opinion Visualization. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Note continued: 3.2. Text Genres and Tasks for Opinion Visualization -- 3.3. Opinion Visualization of Customer Feedback -- 3.4. Opinion Visualization of User Reactions to Large-Scale Events via Microblogs -- 3.5. Visualizing Opinions in Online Conversations -- 3.6. Current and Future Trends in Opinion Visualization -- 4. Conclusion -- References -- ch. 12 Sentiment Analysis with SpagoBI -- 1. Introduction to SpagoBI -- 2. Social Network Analysis with SpagoBI -- 2.1. Main Purpose -- 2.2. Features -- 2.3. Use Case -- 3. Algorithms Used -- 4. Conclusion -- ch. 13 SOMA: The Smart Social Customer Relationship Management Tool: Handling Semantic Variability of Emotion Analysis with Hybrid Technologies -- 1. Introduction -- 2. Definition of Sentiment and Emotion Mining -- 3. Previous Work -- 4.A Silver Standard Corpus for Emotion Classification in Tweets -- 5. General System -- 5.1. Hybrid Operable Platform for Language Management and Extensible Semantics -- 5.2. The Machine Learning Approach. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Note continued: 5.3. The Symbolic Approach -- 6. Results and Evaluation -- 6.1. Tweet Emotion Detection -- 6.2. Tweet Relevance -- 7. Conclusion -- Acknowledgments -- References -- ch. 14 The Human Advantage: Leveraging the Power of Predictive Analytics to Strategically Optimize Social Campaigns -- 1. Introduction -- 2. The Current Philosophy Around Sentiment Analysis -- 3. KRC Research's Digital Content and Sentiment Philosophy -- 3.1. Pretesting Is Crucial -- 3.2. Continuously Learn How to Improve -- 3.3. Use Scientific Sampling Rather Than Reviewing Every Piece of Content -- 3.4. Build Predictive Models -- 4. KRC Research's Sentiment and Analytics Approach -- 5. Case Study -- 5.1. Life Insurance Organization -- 6. Conclusion -- ch. 15 Price-Sensitive Ripples and Chain Reactions: Tracking the Impact of Corporate Announcements with Real-Time Multidimensional Opinion Streaming -- 1. Introduction -- 2. Architecture -- 2.1. Data Sources and Filters. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Note continued: 2.2. Core Natural Language Processing and Opinion Metrics -- 2.3. Opinion Metrics -- 2.4. Indexing -- 2.5. Real-Time Opinion Streaming -- 3. Multidimensional Opinion Metrics -- 3.1. Fine-Grained Multilevel Sentiment -- 3.2. Multidimensional Affect -- 3.3. Irrealis Modality -- 3.4.Comparisons -- 3.5. Topic Tagging -- 4. Discussion -- 5. Conclusion -- Acknowledgments -- References -- ch. 16 Conclusion and Future Directions. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Natural language processing (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computational linguistics. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Social networks. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | COMPUTERS |
| General subdivision | Natural Language Processing. |
| Source of heading or term | bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computational linguistics. |
| Source of heading or term | fast |
| Authority record control number | (OCoLC)fst00871998 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Natural language processing (Computer science) |
| Source of heading or term | fast |
| Authority record control number | (OCoLC)fst01034365 |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Social networks. |
| Source of heading or term | fast |
| Authority record control number | (OCoLC)fst01122678 |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Pozzi, Federico Alberto, |
| Relator term | editor. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Fersini, Elisabetta, |
| Relator term | editor. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Messina, Enza, |
| Relator term | editor. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Liu, Bing, |
| Relator term | editor. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Print version: |
| Title | Sentiment analysis in social networks. |
| Place, publisher, and date of publication | Cambridge, MA : Morgan Kaufmann, 2017 |
| International Standard Book Number | 9780128044124 |
| -- | 0128044128 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | ScienceDirect |
| Uniform Resource Identifier | http://www.sciencedirect.com/science/book/9780128044124 |
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