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E-grāmata: Mining User Generated Content [Taylor & Francis e-book]

Edited by (Tsinghua University, China), Edited by (National University of Singapore, Singapore), Edited by (Katholieke Universiteit Leuven, Belgium)
  • Formāts: 474 pages, 52 Tables, black and white; 47 Illustrations, black and white
  • Sērija : Social Media and Social Computing
  • Izdošanas datums: 28-Jan-2014
  • Izdevniecība: CRC Press Inc
  • ISBN-13: 9780429087615
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Mining User Generated ContentPalielināt
  • Formāts: 474 pages, 52 Tables, black and white; 47 Illustrations, black and white
  • Sērija : Social Media and Social Computing
  • Izdošanas datums: 28-Jan-2014
  • Izdevniecība: CRC Press Inc
  • ISBN-13: 9780429087615
Citas grāmatas par šo tēmu:
Taylor & Francis e-booksMore info about Taylor & Francis e-books
Rokasgrāmatas mājas lapa: https://www.taylorfrancis.com/books/9780429087615
Written for students, researchers, and practitioners, this book on multimedia data mining compiles the latest research on analysis and use of user-generated content (UGC) found on social networks, mobile computing, and cloud computing. Part 1 introduces UGC, and Part 2 describes methods for mining UGC of different medium types. Topics discussed include the social annotation of UGC, social network graph construction and community mining, mining of UGC to assist in music retrieval, and UCG sentiment analysis. Part 3 discusses the mining and searching of various types of UGC, including knowledge extraction, search techniques for UGC, and analysis and annotation of Japanese blogs. The last section presents applications of the use of UGC to support question-answering and information summarization. B&w screenshots are included. Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)

Originating from Facebook, LinkedIn, Twitter, Instagram, YouTube, and many other networking sites, the social media shared by users and the associated metadata are collectively known as user generated content (UGC). To analyze UGC and glean insight about user behavior, robust techniques are needed to tackle the huge amount of real-time, multimedia, and multilingual data. Researchers must also know how to assess the social aspects of UGC, such as user relations and influential users.

Mining User Generated Content is the first focused effort to compile state-of-the-art research and address future directions of UGC. It explains how to collect, index, and analyze UGC to uncover social trends and user habits.

Divided into four parts, the book focuses on the mining and applications of UGC. The first part presents an introduction to this new and exciting topic. Covering the mining of UGC of different medium types, the second part discusses the social annotation of UGC, social network graph construction and community mining, mining of UGC to assist in music retrieval, and the popular but difficult topic of UGC sentiment analysis. The third part describes the mining and searching of various types of UGC, including knowledge extraction, search techniques for UGC content, and a specific study on the analysis and annotation of Japanese blogs. The fourth part on applications explores the use of UGC to support question-answering, information summarization, and recommendations.

Foreword xiii
Preface xv
Editors xix
Contributors xxi
List of Reviewers xxxi
List of Figures xxxiii
List of Tables xxxvii
I Introduction 1(18)
1 Mining User Generated Content and Its Applications
3(16)
Marie-Francine Moens
Juanzi Li
Tat-Seng Chua
1.1 The Web and Web Trends
3(4)
1.1.1 The Emergence of the World Wide Web (WWW): From Connected Computers to Linked Documents
3(2)
1.1.2 The Prevailingness of Web 2.0: From "Read-Only" to Read-and-Write-Interaction
5(1)
1.1.3 What Will Be Next?
6(1)
1.2 Defining User Generated Content
7(2)
1.3 A Brief History of Creating, Searching, and Mining User Generated Content
9(1)
1.4 Goals of the Book
10(1)
1.5 User Generated Content: Concepts and Bottlenecks
11(3)
1.6 Organization of the Book
14(3)
1.7 Mining User Generated Content: Broader Context
17(2)
II Mining Different Media 19(108)
2 Social Annotation
21(22)
Jia Chen
Shenghua Bao
Haofen Wang
Yong Yu
Zhong Su
2.1 Research on Social Annotations
22(1)
2.2 Techniques in Social Annotations
23(8)
2.2.1 Problem Formulation
24(1)
2.2.2 Social Annotation Propagation
25(1)
2.2.2.1 Social Propagation-Multiple Annotations
26(1)
2.2.2.2 Social Propagation-Multiple Link Types
27(1)
2.2.2.3 Social Propagation-Constraint
27(1)
2.2.2.4 General Model
28(1)
2.2.3 Discussion
29(1)
2.2.3.1 Scalability of the Propagation
29(1)
2.2.3.2 Propagation through More Links
30(1)
2.2.3.3 Propagation with More Constraints
30(1)
2.2.3.4 Propagating More Information
31(1)
2.3 Application of Social Annotations
31(10)
2.3.1 Social Annotation for Personalized Search
31(1)
2.3.1.1 Analysis of Folksonomy
32(1)
2.3.1.2 A Personalized Search Framework
33(1)
2.3.1.3 Topic Space Selection
34(1)
2.3.1.4 Interest and Topic Adjusting via a Bipartite Collaborative Link Structure
35(2)
2.3.2 Hierarchical Semantics from Social Annotations
37(1)
2.3.2.1 Algorithm Overview
39(2)
2.4 Conclusion
41(2)
3 Sentiment Analysis in UGC
43(24)
Ning Yu
3.1 Introduction
43(1)
3.2 Background
44(2)
3.2.1 Problem Definition
44(1)
3.2.2 Levels of Granularity
45(1)
3.3 Major Issues in Sentiment Analysis
46(18)
3.3.1 Data Annotation
46(1)
3.3.2 Important Sentiment Features
47(1)
3.3.2.1 Single Word Features
48(1)
3.3.2.2 Part-of-Speech Based Features
50(1)
3.3.2.3 N-Grams, Phrases, and Patterns
52(1)
3.3.2.4 Other Sentiment Features
55(1)
3.3.2.5 Recommendation for Selecting Sentiment Features
57(1)
3.3.3 Sentiment Scoring and Classification
57(1)
3.3.3.1 Ad Hoc Rule-Based Approach
57(1)
3.3.3.2 Supervised Learning Approach
58(1)
3.3.3.3 Semisupervised Learning (Bootstrapping)
60(4)
3.4 Conclusion
64(3)
4 Mining User Generated Data for Music Information Retrieval
67(30)
Markus Schedl
Mohamed Sordo
Noam Koenigstein
Udi Weinberg
4.1 Introduction to Music Information Retrieval (MIR)
68(2)
4.1.1 User Generated Content in MIR Research
68(2)
4.1.2 Organization of the
Chapter
70(1)
4.2 Web Pages
70(6)
4.2.1 Similarity Measurement
72(3)
4.2.2 Information Extraction
75(1)
4.3 Microblogs
76(3)
4.3.1 Similarity Measurement
76(2)
4.3.2 Popularity Estimation
78(1)
4.4 Explicit User Ratings
79(4)
4.4.1 Characteristics of Explicit Rating Datasets
80(1)
4.4.2 Matrix Factorization Models
81(2)
4.5 Peer-to-Peer Networks
83(5)
4.5.1 P2P Data Usage
84(3)
4.5.2 Peer Similarity Measurement
87(1)
4.5.3 Recommendation Systems
87(1)
4.5.4 Popularity Estimation
88(1)
4.6 Social Tags
88(5)
4.6.1 Similarity Measurement
90(2)
4.6.2 Use of Social Tags in MIR
92(1)
4.7 Social Networks
93(1)
4.7.1 Music Recommendation
93(1)
4.7.2 Playlist Generation
93(1)
4.8 Conclusion
94(3)
5 Graph and Network Pattern Mining
97(30)
Jan Ramon
Constantin Comendant
Mostafa Haghir Chehreghani
Yuyi Wang
5.1 Introduction
98(1)
5.2 Basic Concepts
99(3)
5.3 Transactional Graph Pattern Mining
102(15)
5.3.1 The Graph Pattern Mining Problem
102(2)
5.3.2 Basic Pattern Mining Techniques
104(3)
5.3.3 Graph Mining Settings
107(2)
5.3.4 Complexity
109(1)
5.3.4.1 Enumeration Complexity
109(1)
5.3.4.2 Complexity Results
110(1)
5.3.4.3 Optimization Techniques
111(1)
5.3.5 Condensed Representations
112(1)
5.3.5.1 Free and Closed Patterns
113(1)
5.3.5.2 Selection of Informative Patterns
115(1)
5.3.6 Transactional Graph Mining Systems
115(2)
5.4 Single Network Mining
117(8)
5.4.1 Network Models
117(1)
5.4.1.1 Network Property Measures
118(1)
5.4.1.2 Network Models
118(2)
5.4.2 Pattern Matching in a Single Network
120(1)
5.4.2.1 Matching Small Patterns
120(1)
5.4.2.2 Exact Pattern Matching
121(1)
5.4.2.3 Approximative Algorithms for Pattern Matching
121(1)
5.4.2.4 Algorithms for Approximate Pattern Matching
122(1)
5.4.3 Pattern Mining Support Measures
122(2)
5.4.4 Applications
124(1)
5.5 Conclusion
125(1)
5.6 Additional Reading
125(1)
5.7 Glossary
126(1)
5.8 Acknowledgments
126(1)
III Mining and Searching Different Types of UGC 127(96)
6 Knowledge Extraction from Wikis/BBS/Blogs/News Web Sites
129(38)
Jun Zhao
Kang Liu
Guangyou Zhou
Zhenyu Qi
Yang Liu
Xianpei Han
6.1 Introduction
130(5)
6.1.1 Task Descriptions
132(1)
6.1.2 Important Challenges
133(1)
6.1.3 Organization of the
Chapter
134(1)
6.2 Entity Recognition and Expansion
135(8)
6.2.1 Entity Recognition
135(1)
6.2.2 Entity Set Expansion
136(1)
6.2.2.1 Seed Generation
137(1)
6.2.2.2 Entity Extraction
140(1)
6.2.2.3 Result Refinement
143(1)
6.2.3 Summary
143(1)
6.3 Relation Extraction
143(11)
6.3.1 Introduction
143(1)
6.3.2 Predefined Relation Extraction
144(1)
6.3.2.1 Identify Relations between the Given Entities
144(1)
6.3.2.2 Identify Entity Pairs for Given Relation Types
145(1)
6.3.2.3 Evaluations on Predefined Relation Extraction
146(1)
6.3.3 Open Domain Relation Extraction
147(1)
6.3.3.1 Relation Extraction in Structured/Semistructured Web Pages
148(1)
6.3.3.2 Relation Extraction from Unstructured Texts
150(3)
6.3.4 Comparison
153(1)
6.3.5 Summary
154(1)
6.4 Named Entity Disambiguation
154(12)
6.4.1 Task Description
154(2)
6.4.2 Evaluation of Entity Disambiguation
156(1)
6.4.2.1 WePS Evaluation
156(1)
6.4.2.2 TAC KBP Evaluation
156(1)
6.4.3 Clustering-Based Entity Disambiguation
157(1)
6.4.3.1 Entity Mention Similarity Computation Based on Textual Features
157(1)
6.4.3.2 Entity Mention Similarity Computation Based on Social Networks
158(1)
6.4.3.3 Entity Mention Similarity Computation Based on Background Knowledge
158(3)
6.4.4 Entity-Linking Based Entity Disambiguation
161(1)
6.4.4.1 Independent Entity Linking
161(1)
6.4.4.2 Collective Entity Linking
163(2)
6.4.5 Summary and Future Work
165(1)
6.5 Conclusion
166(1)
7 User Generated Content Search
167(22)
Roi Blanco
Manuel Eduardo Ares Brea
Christina Lioma
7.1 Introduction
168(1)
7.2 Overview of State-of-the-Art
168(7)
7.2.1 Blogs
168(1)
7.2.1.1 Blog Indexing
169(1)
7.2.1.2 Ranking Blog Posts
170(1)
7.2.1.3 Blog-Specific Features
170(1)
7.2.1.4 Blog Representations
171(1)
7.2.2 Microblogs
171(1)
7.2.2.1 Microblog Expansion
172(1)
7.2.2.2 Microblog Search Engines
173(1)
7.2.2.3 Microblogs as Aids to Standard Searches
173(1)
7.2.3 Social Tags
173(1)
7.2.3.1 Social Tags for Text Search
174(1)
7.2.3.2 Social Tags for Image Search
175(1)
7.3 Social Tags for Query Expansion
175(11)
7.3.1 Problem Formulation
176(1)
7.3.1.1 Jin's Method
177(2)
7.3.2 Experimental Evaluation
179(1)
7.3.2.1 Methodology and Settings
179(1)
7.3.2.2 Parameter Tuning
180(1)
7.3.2.3 Findings and Discussion
183(3)
7.4 Conclusion
186(3)
8 Annotating Japanese Blogs with Syntactic and Affective Information
189(34)
Michal Ptaszynski
Yoshio Momouchi
Jacek Maciejewski
Pawel Dybala
Rafal Rzepka
Kenji Araki
8.1 Introduction
190(1)
8.2 Related Research
191(8)
8.2.1 Large-Scale Corpora
191(4)
8.2.2 Emotion Corpora
195(4)
8.3 YACIS Corpus Compilation
199(4)
8.4 YACIS Corpus Annotation
203(14)
8.4.1 Annotation Tools
203(1)
8.4.1.1 Syntactic Information Annotation Tools
203(1)
8.4.1.2 Affective Information Annotation Tools .
204(4)
8.4.2 YACIS Corpus Statistics
208(1)
8.4.2.1 Syntactic Information
208(1)
8.4.2.2 Affective Information
211(6)
8.5 Applications
217(2)
8.5.1 Emotion Object Ontology Generation
217(1)
8.5.2 Moral Consequence Retrieval
218(1)
8.6 Discussion
219(1)
8.7 Conclusions and Future Work
219(2)
8.8 Acknowledgments
221(2)
IV Applications 223(106)
9 Question-Answering of UGC
225(34)
Chin-Yew Lin
9.1 Introduction
225(4)
9.2 Question-Answering by Searching Questions
229(2)
9.3 Question Search
231(12)
9.3.1 Query Likelihood Language Models
232(3)
9.3.2 Exploiting Category Information
235(3)
9.3.3 Structured Question Search
238(1)
9.3.3.1 Topic-Focus Mixture Model
239(1)
9.3.3.2 Entity-Based Translation Model
239(1)
9.3.3.3 Syntactic Tree Matching
240(3)
9.4 Question Quality, Answer Quality, and User Expertise
243(12)
9.4.1 Defining Quality and Expertise
244(2)
9.4.2 Indicators of Quality and Expertise
246(3)
9.4.3 Modeling Quality and Expertise
249(1)
9.4.3.1 Question Utility-Aware Retrieval Model
249(1)
9.4.3.2 Answer Quality-Aware Retrieval Model
250(1)
9.4.3.3 Expertise-Aware Retrieval Model
250(1)
9.4.3.4 Quality and Expertise-Aware Retrieval Model
252(3)
9.5 Conclusion
255(4)
10 Summarization of UGC
259(28)
Dominic Rout
Kalina Bontcheva
10.1 Introduction
259(1)
10.2 Automatic Text Summarization: A Brief Overview
260(2)
10.3 Why Is User Generated Content a Challenge?
262(3)
10.4 Text Summarization of UGC
265(6)
10.4.1 Summarizing Online Reviews
265(2)
10.4.2 Blog Summarization
267(1)
10.4.3 Summarizing Very Short UGC
268(3)
10.5 Structured, Sentiment-Based Summarization of UGC
271(2)
10.6 Keyword-Based Summarization of UGC
273(1)
10.7 Visual Summarization of UGC
274(4)
10.8 Evaluating UGC Summaries
278(2)
10.8.1 Training Data, Evaluation, and Crowdsourcing
279(1)
10.9 Outstanding Challenges
280(4)
10.9.1 Spatio-Temporal Summaries
280(2)
10.9.2 Exploiting Implicit UGC Semantics
282(1)
10.9.3 Multilinguality
282(1)
10.9.4 Personalization
283(1)
10.9.5 Evaluation
283(1)
10.10 Conclusion
284(1)
10.11 Acknowledgments
285(2)
11 Recommender Systems
287(32)
Claudio Lucchese
Cristina Ioana Muntean
Raffaele Perego
Fabrizio Silvestri
Hossein Vahabi
Rossano Venturini
11.1 Recommendation Techniques
289(9)
11.1.1 Collaborative Filtering-Based Recommendations
290(2)
11.1.2 Demographic Recommendations
292(1)
11.1.3 Content-Based Recommendations
293(1)
11.1.4 Knowledge-Based Recommendations
294(2)
11.1.5 Hybrid Methods
296(2)
11.2 Exploiting Query Logs for Recommending Related Queries
298(5)
11.2.1 Query Logs as Sources of Information
299(1)
11.2.2 A Graph-Based Model for Query Suggestion
300(3)
11.3 Exploiting Photo Sharing and Wikipedia for Touristic Recommendations
303(4)
11.3.1 Flickr and Wikipedia as Sources of Information
303(1)
11.3.2 From Flickr and Wikipedia to Touristic Recommendations via Center-Piece Computation
304(3)
11.4 Exploiting Twitter and Wikipedia for News Recommendations
307(6)
11.4.1 The Blogosphere as a Source of Information
308(2)
11.4.2 Using the Real-Time Web for Pergonalized News Recommendations
310(3)
11.5 Recommender Systems for Tags
313(4)
11.5.1 Social Tagging Platforms as Sources of Information
313(2)
11.5.2 Recommending Correctly Spelled Tags
315(2)
11.6 Conclusion
317(2)
12 Conclusions and a Road Map for Future Developments
319(10)
Marie-Francine Moens
Juanzi Li
Tat-Seng Chua
12.1 Summary of the Main Findings
319(2)
12.2 Road Map
321(8)
12.2.1 Processing Community Languages
321(1)
12.2.2 Image and Video Processing
322(1)
12.2.3 Audio Processing
323(1)
12.2.4 Aggregation and Linking of UGC
323(2)
12.2.5 Legal Considerations
325(2)
12.2.6 Information Credibility
327(2)
Bibliography 329(68)
Index 397
Marie-Francine Moens, Juanzi Li, Tat-Seng Chua
Permanent link: https://www.kriso.lv/db/9780429087615_pe.html
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