
Sentiment Analysis in Social Networks
Description
Key Features
- 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
Readership
Academic and industry researchers in artificial intelligence, natural language processing, social networking, networking, and big data
Table of Contents
- 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
Product details
- No. of pages: 284
- Language: English
- Copyright: © Morgan Kaufmann 2016
- Published: September 15, 2016
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9780128044124
- eBook ISBN: 9780128044384
About the Authors
Federico Pozzi
Affiliations and Expertise
Elisabetta Fersini
Affiliations and Expertise
Enza Messina
Affiliations and Expertise
Bing Liu
Affiliations and Expertise
Ratings and Reviews
Latest reviews
(Total rating for all reviews)
AlexanderGilgur Wed May 15 2019
A good overview of modern approaches to sentiment analysis
I really enjoy this book. It combines a good analysis of social networks with principles of mathematical analysis of sentiment.
Prof. P. Tue Dec 12 2017
Prof Deepali Pande
Best book to learn Opinion Mining.