Sentiment Analysis in Social Networks
View on ScienceDirect

Sentiment Analysis in Social Networks

1st Edition - September 15, 2016

Write a review

  • Authors: Federico Pozzi, Elisabetta Fersini, Enza Messina, Bing Liu
  • Paperback ISBN: 9780128044124
  • eBook ISBN: 9780128044384

Purchase options

Purchase options
Available
DRM-free (EPub, Mobi, PDF)
eBook Format Help
Sales tax will be calculated at check-out

Institutional Subscription

Support CenterReturns & Refunds
Free Global Shipping
No minimum order

Description

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

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

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 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

Prof. Bing Liu is a professor of computer science at the University of Illinois at Chicago. He received his PhD in Arti cial Intelligence from the University of Edinburgh. His current research interests include sentiment analysis and opinion mining, data mining, machine learning, and natural language processing. He has published extensively in top conferences and journals, and is the author of three books: Sentiment Analysis and Opinion Mining (2012), Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (first edition, 2007; second edition, 2011), and Sentiment Analysis: Mining Opinions, Sentiments and Emotions (2015). Two of his papers received 10-year test-of-time awards from KDD, the premier conference of data mining and big data. His research has also been cited on the front page of the New York Times. He currently serves as the Chair of ACM SIGKDD, and is an Fellow of ACM, AAAI, and IEEE.

Affiliations and Expertise

University of Illinois at Chicago, USA

Ratings and Reviews

Write a review

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.