Social media mining and social network analysis is a hot research area, and the 14 chapters of this book provide a good interdisciplinary overview of the field. As sentiment analysis and opinion mining are key topics in this field, the book starts with a chapter on extracting sentiment patterns from syntactic graphs, where the authors propose a novel method for representing a text, based on graphs, extracted from sentence linguistic parse trees. Following this chapter, the next focuses on mobile context data mining, as smartphones and touch tablets are becoming increasingly popular; and the authors illustrate two context mining methods which process multiple types of context data (e.g. location, accelerometer, etc.).
Chapters 3 to 7 cover topics around user-generated content. Thus Chapter 3 reports on techniques and applications of tag clustering. Chapter 4 focuses on social interaction based on two case studies of popular news topics. Chapter 5 presents a systematic survey of non-Bayesian- and Bayesian-based approaches to the web community-discovering problem, while Chapter 6 discusses a tree-based mining approach to discover important friend groups in a social network. Chapter 7 presents a novel news document summarisation system (NeDocS), which focuses on generating succinct, non-redundant summaries by means of data mining and knowledge discovery processes driven by messages posted on social networks.
In Chapter 8 the authors introduce a framework for a real-scale task-oriented menu system for mobile service navigation. Chapters 9 to 11 concentrate on aspects of extracting information from web-based data, including social tagging systems, global community extraction and local community detection. Chapter 12 focuses, as does Chapter 2, on the power of smart phones in supporting social interaction, using the example of a university campus environment. Chapter 13 deals with aspects of detecting similarities between short micro-blogs: the authors utilise three approaches (term-based, WordNet-based semantic and topic-based). Finally, Chapter 14 focuses on the proper relationship among users, resources and tags within social annotation-based recommendation researches.
Some chapters contain an explanation of key terms and definitions. The book is of excellent quality and overall is well written. It can be recommended as a good introduction for a broad audience to gain insight into some challenging topics in social computing.
