Sentiment analysis in social networks /

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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 anal...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Pozzi, Federico Alberto (Editor ), Fersini, Elisabetta (Editor ), Messina, Enza (Editor ), Liu, Bing (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge, MA : Morgan Kaufmann, 2017.
Temas:
Natural language processing (Computer science)
Computational linguistics.
Social networks.
Traitement automatique des langues naturelles.
Linguistique informatique.
Réseaux sociaux.
computational linguistics.
COMPUTERS > Natural Language Processing.
Computational linguistics
Social networks
Acceso en línea:Texto completo
Texto completo
Tabla de Contenidos:
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

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