Sentiment Analysis in Social Networks

1st Edition - September 15, 2016

Authors: Federico Alberto Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu

Paperback ISBN:

9 7 8 - 0 - 1 2 - 8 0 4 4 1 2 - 4

eBook ISBN:

9 7 8 - 0 - 1 2 - 8 0 4 4 3 8 - 4

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… Read more

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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 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

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

Federico Alberto Pozzi

Dr. Federico Alberto Pozzi received the Ph.D. in Computer Science at the University of Milano - Bicocca (Italy). His Ph.D. thesis is focused on Probabilistic Relational Models for Sentiment Analysis in Social Networks. His research interests primarily focus on Data Mining, Text Mining, Machine Learning, Natural Language Processing and Social Network Analysis, in particular applied to Sentiment Analysis and Community Discovery in Social Networks. He currently works at SAS Institute (Italy) as Senior Solutions Specialist - Integrated Marketing Management & Analytics.

Affiliations and expertise

SAS Institute, Italy

Elisabetta Fersini

Dr. Elisabetta Fersini is currently a postdoctoral research fellow at the University of Milano - Bicocca (Italy). Her research activity is mainly focused on statistical relational learning with particular interests in supervised and unsupervised classification. The research activity finds application to Web/Text mining, Sentiment Analysis, Social Network Analysis, e-Justice and Bioinformatics. She actively participated to several national and international research projects. She has been an evaluator for international research projects and member of different scientific committees. She co-founded an academic spin-off specialized in sentiment analysis and community discovery in social networks.

Affiliations and expertise

University of Milano-Bicocca, Italy

Enza Messina

Prof. Enza Messina is a Professor in Operations Research at the Department of Informatics Systems and Communications, University of Milano-Bicocca, where she leads the research Laboratory MIND (Models in decision making and data analysis). She holds a Ph.D. in Computational Mathematics and Operations Research from the University of Milano. Her research activity is mainly focused on decision models under uncertainty and more recently on statistical relational models for data analysis and knowledge extraction. In particular, she developed relational classi_x000C_cation and clustering models that finds applications in different domains such as systems biology, e-justice, text mining and social network analysis.

Affiliations and expertise

University of Milano-Bicocca, Italy

Bing Liu

Dr Bing Liu is an Associate Professor at the College of Agriculture, Nanjing Agricultural University, China. He received his PhD in Information Agriculture in 2016 from Nanjing Agricultural University. His research areas include extreme climate effects on crop growth, yield, and quality; agricultural systems modelling; and climate change impact assessment and adaptation.

Affiliations and expertise

Associate Professor, College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, China