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E-raamat: Sentiment Analysis in Social Networks

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(SAS Institute, Italy), (Associate Professor, College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, China), (University of Milano-Bicocca, Italy), (University of Milano-Bicocca, Italy)
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  • Ilmumisaeg: 06-Oct-2016
  • Kirjastus: Morgan Kaufmann Publishers In
  • Keel: eng
  • ISBN-13: 9780128044384
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Sentiment Analysis in Social NetworksSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 06-Oct-2016
  • Kirjastus: Morgan Kaufmann Publishers In
  • Keel: eng
  • ISBN-13: 9780128044384
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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 methodologiesProvides insights into opinion spamming, reasoning, and social network analysisShows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequencesServes as a one-stop reference for the state-of-the-art in social media analyticsTakes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologiesProvides insights into opinion spamming, reasoning, and social network miningShows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequencesServes as a one-stop reference for the state-of-the-art in social media analytics

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This book provides an exploration of the latest and most relevant research in sentiment analysis that debates the advantages and disadvantages of applying its practice to social networks
Contributors xi
Editors' Biographies xv
Preface xvii
Acknowledgments xix
Chapter 1 Challenges of Sentiment Analysis in Social Networks: An Overview
1(12)
1 Background
1(3)
2 Sentiment Analysis in Social Networks: A New Research Approach
4(1)
3 Sentiment Analysis Characteristics
5(4)
3.1 Sentiment Categorization: Objective Versus Subjective Sentences
5(1)
3.2 Levels of Analysis
6(1)
3.3 Regular Versus Comparative Opinion
7(1)
3.4 Explicit Versus Implicit Opinions
7(1)
3.5 The Role of Semantics
8(1)
3.6 Dealing with Figures of Speech
8(1)
3.7 Relationships in Social Networks
9(1)
4 Applications
9(4)
References
10(3)
Chapter 2 Beyond Sentiment: How Social Network Analytics Can Enhance Opinion Mining and Sentiment Analysis
13(18)
1 Introduction
13(1)
2 Definitions and History of Online Social Networks
14(2)
2.1 What Exactly Is an Online Social Network?
14(1)
2.2 Brief History of Online Social Networks
15(1)
3 Are Online Social Networks All the Same? Features and Metrics
16(2)
3.1 Types of User-Generated Content
16(1)
3.2 Types of Relationships Between Users
17(1)
3.3 Indexes and Metrics to Analyze Data Collected Through Online Social Networks
17(1)
4 Psychological and Motivational Factors for People to Share Opinions and to Express Themselves on Social Networks
18(1)
4.1 Need to Belong
18(1)
4.2 Need for Cognition
19(1)
4.3 Self-Presentation and Impression Management
19(1)
5 From Sociology Principles to Social Networks Analytics
19(2)
5.1 Tie Strengths
20(1)
5.2 Homophily or Similarity Breeds Connection
20(1)
5.3 Source Credibility
20(1)
6 How Can Social Network Analytics Improve Sentiment Analysis on Online Social Networks?
21(4)
6.1 What Is Social Network Analysis?
22(1)
6.2 How to Integrate Social Network Analytics in Sentiment Analysis: Some Examples
23(2)
7 Conclusion and Future Directions
25(6)
References
25(6)
Chapter 3 Semantic Aspects in Sentiment Analysis
31(18)
1 Introduction
31(1)
2 Semantic Resources for Sentiment Analysis
32(8)
2.1 Classical Resources on Sentiment
32(2)
2.2 Beyond the Polarity Valence: Emotion Lexica, Ontologies, and Psycholinguistic Resources
34(4)
2.3 Social Media Corpora Annotated for Sentiment and Fine Emotion Categories
38(2)
3 Using Semantics in Sentiment Analysis
40(4)
3.1 Lexical Information
40(1)
3.2 Distributional Semantics
41(1)
3.3 Entities, Properties, and Relations
41(1)
3.4 Concept-Level Sentiment Analysis: Reasoning with Semantics
42(2)
4 Conclusions
44(5)
Chapter 4 Linked Data Models for Sentiment and Emotion Analysis in Social Networks
49(22)
1 Introduction
49(1)
2 Marl: A Vocabulary for Sentiment Annotation
50(2)
3 Onyx: A Vocabulary for Emotion Annotation
52(4)
3.1 Onyx Extensibility: Vocabularies
55(1)
3.2 Emotion Markup Language
56(1)
4 Linked Data Corpus Creation for Sentiment Analysis
56(5)
4.1 Sentiment Corpus
58(2)
4.2 Emotion Corpus
60(1)
5 Linked Data Lexicon Creation for Sentiment Analysis
61(1)
5.1 Sentiment Lexicon
61(1)
5.2 Emotion Lexicon
62(1)
6 Sentiment and Emotion Analysis Services
62(2)
7 Case Study: Generation of a Domain-Specific Sentiment Lexicon
64(1)
8 Conclusions
65(6)
Acknowledgments
66(1)
References
66(5)
Chapter 5 Sentic Computing for Social Network Analysis
71(20)
1 Introduction
71(1)
2 Related Work
72(3)
3 Affective Characterization
75(2)
4 Applications
77(8)
4.1 Troll Filtering
77(2)
4.2 Social Media Marketing
79(3)
4.3 A Model for Sentiment Classification in Twitter
82(3)
5 Future Trends and Directions
85(1)
6 Conclusion
86(5)
References
86(5)
Chapter 6 Sentiment Analysis in Social Networks: A Machine Learning Perspective
91(22)
1 Introduction
91(1)
2 Polarity Classification in Online Social Networks: The Key Elements
92(2)
3 Polarity Classification: Natural Language and Relationships
94(9)
3.1 Leveraging Natural Language
94(6)
3.2 Leveraging Natural Language and Relationships
100(3)
4 Applications
103(1)
5 Future Directions
104(1)
6 Conclusion
105(8)
References
105(8)
Chapter 7 Irony, Sarcasm, and Sentiment Analysis
113(16)
1 Introduction
113(1)
2 Irony and Sarcasm Detection
114(5)
2.1 Irony Detection
115(2)
2.2 Sarcasm Detection
117(2)
3 Figurative Language and Sentiment Analysis
119(5)
3.1 Sentiment Polarity Classification at Evalita 2014
119(2)
3.2 Sentiment Analysis in Twitter at SemEval 2014 and 2015
121(1)
3.3 Sentiment Analysis of Figurative Language in Twitter at SemEval 2015
122(2)
4 Future Trends and Directions
124(1)
5 Conclusions
124(5)
Acknowledgments
125(1)
References
125(4)
Chapter 8 Suggestion Mining From Opinionated Text
129(12)
1 Introduction
129(1)
2 Sentiments and Suggestions
130(1)
3 Task Definition and Typology of Suggestions
131(1)
4 Datasets
132(2)
5 Approaches for Suggestion Detection
134(2)
5.1 Linguistic Observations in Suggestions
134(1)
5.2 Detection of Suggestions for Improvements
135(1)
5.3 Detection of Suggestions to Fellow Customers
135(1)
6 Applications
136(1)
7 Future Trends and Directions
137(1)
8 Summary
138(3)
Acknowledgments
138(1)
References
138(3)
Chapter 9 Opinion Spam Detection in Social Networks
141(16)
1 Introduction
141(1)
2 Related Work
141(1)
3 Review Spammer Detection Leveraging Reviewing Burstiness
142(5)
3.1 Burst Detection
142(1)
3.2 Spammer Detection Under Review Bursts
143(4)
4 Detecting Campaign Promoters on Twitter
147(3)
4.1 Campaign Promoter Modeling Using Typed Markov Random Fields
147(2)
4.2 Inference
149(1)
5 Spotting Spammers Using Collective Positive-Unlabeled Learning
150(5)
5.1 Problem Definition
150(2)
5.2 Collective Classification
152(1)
5.3 Model Evaluation
153(1)
5.4 Trends and Directions
154(1)
6 Conclusion
155(2)
Acknowledgments
155(1)
References
155(2)
Chapter 10 Opinion Leader Detection
157(14)
1 Introduction
157(1)
2 Problem Definition
157(1)
3 Approaches
158(8)
3.1 Measures Based on Network Structure
158(4)
3.2 Methods Based on Interaction
162(2)
3.3 Methods Based on Content Mining
164(1)
3.4 Methods Based on Content and Interaction
165(1)
4 Discussion
166(2)
5 Conclusions
168(3)
References
169(2)
Chapter 11 Opinion Summarization and Visualization
171(18)
1 Introduction
171(1)
2 Opinion Summarization
171(5)
2.1 Challenges
172(1)
2.2 Evaluation
173(1)
2.3 Opinion Summarization Approaches
174(2)
3 Opinion Visualization
176(9)
3.1 Challenges for Opinion Visualization
177(1)
3.2 Text Genres and Tasks for Opinion Visualization
177(2)
3.3 Opinion Visualization of Customer Feedback
179(2)
3.4 Opinion Visualization of User Reactions to Large-Scale Events via Microblogs
181(1)
3.5 Visualizing Opinions in Online Conversations
181(3)
3.6 Current and Future Trends in Opinion Visualization
184(1)
4 Conclusion
185(4)
References
185(4)
Chapter 12 Sentiment Analysis with SpagoBI
189(8)
1 Introduction to SpagoBI
189(1)
2 Social Network Analysis with SpagoBI
190(4)
2.1 Main Purpose
190(1)
2.2 Features
190(2)
2.3 Use Case
192(2)
3 Algorithms Used
194(1)
4 Conclusion
195(2)
Chapter 13 SOMA: The Smart Social Customer Relationship Management Tool: Handling Semantic Variability of Emotion Analysis with Hybrid Technologies
197(14)
1 Introduction
197(1)
2 Definition of Sentiment and Emotion Mining
198(1)
3 Previous Work
198(1)
4 A Silver Standard Corpus for Emotion Classification in Tweets
199(2)
5 General System
201(4)
5.1 Hybrid Operable Platform for Language Management and Extensible Semantics
201(1)
5.2 The Machine Learning Approach
202(1)
5.3 The Symbolic Approach
203(2)
6 Results and Evaluation
205(3)
6.1 Tweet Emotion Detection
205(2)
6.2 Tweet Relevance
207(1)
7 Conclusion
208(3)
Acknowledgments
208(1)
References
209(2)
Chapter 14 The Human Advantage: Leveraging the Power of Predictive Analytics to Strategically Optimize Social Campaigns
211(12)
1 Introduction
211(2)
2 The Current Philosophy Around Sentiment Analysis
213(1)
3 KRC Research's Digital Content and Sentiment Philosophy
213(6)
3.1 Pretesting Is Crucial
215(1)
3.2 Continuously Learn How to Improve
216(1)
3.3 Use Scientific Sampling Rather Than Reviewing Every Piece of Content
217(1)
3.4 Build Predictive Models
218(1)
4 KRC Research's Sentiment and Analytics Approach
219(1)
5 Case Study
220(2)
5.1 Life Insurance Organization
220(2)
6 Conclusion
222(1)
Chapter 15 Price-Sensitive Ripples and Chain Reactions: Tracking the Impact of Corporate Announcements with Real-Time Multidimensional Opinion Streaming
223(16)
1 Introduction
223(2)
2 Architecture
225(3)
2.1 Data Sources and Filters
225(1)
2.2 Core Natural Language Processing and Opinion Metrics
226(1)
2.3 Opinion Metrics
227(1)
2.4 Indexing
227(1)
2.5 Real-Time Opinion Streaming
227(1)
3 Multidimensional Opinion Metrics
228(5)
3.1 Fine-Grained Multilevel Sentiment
228(2)
3.2 Multidimensional Affect
230(1)
3.3 Irrealis Modality
231(1)
3.4 Comparisons
232(1)
3.5 Topic Tagging
233(1)
4 Discussion
233(2)
5 Conclusion
235(4)
Acknowledgments
235(1)
References
235(4)
Chapter 16 Conclusion and Future Directions
239(4)
Author Index 243(14)
Subject Index 257
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. 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. 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. 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.
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