Chapman and Hall/CRC
474 pages | 47 B/W Illus.
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.
"This book is timely in collating the experiences and progress in the user-generated content (UGC) area. … this book is contemporary and provides insights into the UGC work in a comprehensible way. It will be well appreciated by researchers, academicians, and practitioners."
—Computing Reviews, June 2015
Mining of User Generated Content and Its Applications Marie-Francine Moens, Juanzi Li, and Tat-Seng Chua
The Web and Web Trends
Defining User Generated Content
A Brief History of Creating, Searching and Mining User Generated Content
Goals of the Book
User Generated Content: Concepts and Bottlenecks
Organization of the Book
Mining User Generated Content: Broader Context
Mining Different Media
Social Annotation Jia Chen, Shenghua Bao, Haofen Wang, and Yong Yu
Research on Social Annotations
Techniques in Social Annotations
Application of Social Annotations
Sentiment Analysis in UGC Ning Yu
Major Issues in Sentiment Analysis
Mining User Generated Data for Music Information Retrieval Markus Schedl, Mohamed Sordo, Noam Koenigstein, and Udi Weinsberg
Introduction to Music Information Retrieval
Explicit User Ratings
Graph and Network Pattern Mining Jan Ramon, Constantin Comendant, Mostafa Haghir Chehreghani, and Yuyi Wang
Transactional Graph Pattern Mining
Single Network Mining
Mining and Searching Different Types of UGC
Knowledge Extraction from Wiki/BBS/Blogs/News Websites Jun Zhao, Kang Liu, Guangyou Zhou, Xianpei Han, Zhenyu Qi, and Yang Liu
Entity Recognition and Expansion
Named Entity Disambiguation
User Generated Content Search Roi Blanco, Manuel Eduardo Ares Brea, and Christina Lioma
Overview of State of the Art
Social Tags for Query Expansion
Annotating Japanese Blogs with Syntactic and Affective Information Michal Ptaszynski, Yoshio Momouchi, Jacek Maciejewski, Pawel Dybala, Rafal Rzepka, and Kenji Araki
YACIS Corpus Compilation
YACIS Corpus Annotation
Conclusions and Future Work
Question Answering of UGC Chin-Yew Lin
Question Answering by Searching Questions?
Question Quality, Answer Quality, and User Expertise
Summarization of UGC Dominic Rout and Kalina Bontcheva
Automatic Text Summarization: A Brief Overview
Why Is User Generated Content a Challenge?
Text Summarization of UGC
Structured, Sentiment-Based Summarization of UGC
Keyword-based Summarization of UGC
Visual Summarization of UGC
Evaluating UGC Summaries
Recommender Systems Claudio Lucchese, Cristina Ioana Muntean, Raffaele Perego, and Fabrizio Silvestri
Exploiting Query Logs for Recommending Related Queries
Exploiting Photo Sharing and Wikipedia for Touristic Recommendations
Exploiting Twitter and Wikipedia for News Recommendation
Recommender Systems for Tags
Conclusions and a Roadmap for Future Developments Marie-Francine Moens, Juanzi Li, and Tat-Seng Chua
Summary of the Main Findings