Sentiment Analysis in Social Networks 1st Edition by Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu ISBN 9780128044384 0128044381
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Sentiment Analysis in Social Networks 1st Edition by Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu – Ebook PDF Instant Download/Delivery: 9780128044384 ,0128044381
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ISBN 10: 0128044381
ISBN 13: 9780128044384
Author: Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu
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
Sentiment Analysis in Social Networks 1st Edition Table of contents:
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
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Federico Alberto Pozzi,Elisabetta Fersini,Enza Messina,Bing Liu,Sentiment Analysis,Social Networks
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