Sentiment Analysis in Social Networks

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 methodologies
• Provides insights into opinion spamming, reasoning, and social network analysis
• Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences
• Serves as a one-stop reference for the state-of-the-art in social media analytics

• Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies
• Provides insights into opinion spamming, reasoning, and social network mining
• Shows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequences
• Serves as a one-stop reference for the state-of-the-art in social media analytics

Academic and industry researchers in artificial intelligence, natural language processing, social networking, networking, and big data

• Editors’ Biographies
• Preface
• Acknowledgments
• Chapter 1: Challenges of Sentiment Analysis in Social Networks: An Overview
  • Abstract
  • 1 Background
  • 2 Sentiment Analysis in Social Networks: A New Research Approach
  • 3 Sentiment Analysis Characteristics
  • 4 Applications
• Chapter 2: Beyond Sentiment: How Social Network Analytics Can Enhance Opinion Mining and Sentiment Analysis
  • Abstract
  • 1 Introduction
  • 2 Definitions and History of Online Social Networks
  • 3 Are Online Social Networks All the Same? Features and Metrics
  • 4 Psychological and Motivational Factors for People to Share Opinions and to Express Themselves on Social Networks
  • 5 From Sociology Principles to Social Networks Analytics
  • 6 How Can Social Network Analytics Improve Sentiment Analysis on Online Social Networks?
  • 7 Conclusion and Future Directions
• Chapter 3: Semantic Aspects in Sentiment Analysis
  • Abstract
  • 1 Introduction
  • 2 Semantic Resources for Sentiment Analysis
  • 3 Using Semantics in Sentiment Analysis
  • 4 Conclusions
• Chapter 4: Linked Data Models for Sentiment and Emotion Analysis in Social Networks
  • Abstract
  • Acknowledgments
  • 1 Introduction
  • 2 Marl: A Vocabulary for Sentiment Annotation
  • 3 Onyx: A Vocabulary for Emotion Annotation
  • 4 Linked Data Corpus Creation for Sentiment Analysis
  • 5 Linked Data Lexicon Creation for Sentiment Analysis
  • 6 Sentiment and Emotion Analysis Services
  • 7 Case Study: Generation of a Domain-Specific Sentiment Lexicon
  • 8 Conclusions
• Chapter 5: Sentic Computing for Social Network Analysis
  • Abstract
  • 1 Introduction
  • 2 Related Work
  • 3 Affective Characterization
  • 4 Applications
  • 5 Future Trends and Directions
  • 6 Conclusion
• Chapter 6: Sentiment Analysis in Social Networks: A Machine Learning Perspective
  • Abstract
  • 1 Introduction
  • 2 Polarity Classification in Online Social Networks: The Key Elements
  • 3 Polarity Classification: Natural Language and Relationships
  • 4 Applications
  • 5 Future Directions
  • 6 Conclusion
• Chapter 7: Irony, Sarcasm, and Sentiment Analysis
  • Abstract
  • Acknowledgments
  • 1 Introduction
  • 2 Irony and Sarcasm Detection
  • 3 Figurative Language and Sentiment Analysis
  • 4 Future Trends and Directions
  • 5 Conclusions
• Chapter 8: Suggestion Mining From Opinionated Text
  • Abstract
  • Acknowledgments
  • 1 Introduction
  • 2 Sentiments and Suggestions
  • 3 Task Definition and Typology of Suggestions
  • 4 Datasets
  • 5 Approaches for Suggestion Detection
  • 6 Applications
  • 7 Future Trends and Directions
  • 8 Summary
• Chapter 9: Opinion Spam Detection in Social Networks
  • Abstract
  • Acknowledgments
  • 1 Introduction
  • 2 Related Work
  • 3 Review Spammer Detection Leveraging Reviewing Burstiness
  • 4 Detecting Campaign Promoters on Twitter
  • 5 Spotting Spammers Using Collective Positive-Unlabeled Learning
  • 6 Conclusion
• Chapter 10: Opinion Leader Detection
  • Abstract
  • 1 Introduction
  • 2 Problem Definition
  • 3 Approaches
  • 4 Discussion
  • 5 Conclusions
• Chapter 11: Opinion Summarization and Visualization
  • Abstract
  • 1 Introduction
  • 2 Opinion Summarization
  • 3 Opinion Visualization
  • 4 Conclusion
• Chapter 12: Sentiment Analysis With SpagoBI
  • Abstract
  • 1 Introduction to SpagoBI
  • 2 Social Network Analysis With SpagoBI
  • 3 Algorithms Used
  • 4 Conclusion
• Chapter 13: SOMA: The Smart Social Customer Relationship Management Tool: Handling Semantic Variability of Emotion Analysis With Hybrid Technologies
  • Abstract
  • Acknowledgments
  • 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
  • 6 Results and Evaluation
  • 7 Conclusion
• Chapter 14: The Human Advantage: Leveraging the Power of Predictive Analytics to Strategically Optimize Social Campaigns
  • Abstract
  • 1 Introduction
  • 2 The Current Philosophy Around Sentiment Analysis
  • 3 KRC Research’s Digital Content and Sentiment Philosophy
  • 4 KRC Research’s Sentiment and Analytics Approach
  • 5 Case Study
  • 6 Conclusion
• Chapter 15: Price-Sensitive Ripples and Chain Reactions: Tracking the Impact of Corporate Announcements With Real-Time Multidimensional Opinion Streaming
  • Abstract
  • Acknowledgments
  • 1 Introduction
  • 2 Architecture
  • 3 Multidimensional Opinion Metrics
  • 4 Discussion
  • 5 Conclusion
• Chapter 16: Conclusion and Future Directions
  • Abstract
• Author Index
• Subject Index