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082 04 006.3/12
082 04 006.3/12|223
245 00 Sentiment analysis in social networks /|cedited by
Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina,
Bing Liu.
264 1 Cambridge, MA :|bMorgan Kaufmann,|c2017.
264 4 |c©2017
300 1 online resource
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
504 Includes bibliographical references and index.
505 0 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 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 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 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 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 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 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 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 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.
520 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.
588 0 Online resource, title from PDF title page (EBSCO, viewed
October 15, 2016).
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
650 0 Natural language processing (Computer science)
650 0 Computational linguistics.
650 0 Social networks.
650 6 Traitement automatique des langues naturelles.
650 6 Linguistique informatique.
650 6 Réseaux sociaux.
650 7 computational linguistics.|2aat
650 7 Computational linguistics|2fast
650 7 Natural language processing (Computer science)|2fast
650 7 Social networks|2fast
700 1 Pozzi, Federico Alberto,|eeditor.
700 1 Fersini, Elisabetta,|eeditor.
700 1 Messina, Enza,|eeditor.
700 1 Liu, Bing,|eeditor.
776 08 |iPrint version:|tSentiment analysis in social networks.
|dCambridge, MA : Morgan Kaufmann, 2017|z9780128044124
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