Text mining applications have experienced tremendous advances because of web 2.0 and social
networking applications. Recent advances in hardware and software technology have lead to a
number of unique scenarios where text mining algorithms are learned. Mining Text Data
introduces an important niche in the text analytics field and is an edited volume contributed
by leading international researchers and practitioners focused on social networks & data
mining. This book contains a wide swath in topics across social networks & data mining. Each
chapter contains a comprehensive survey including the key research content on the topic and
the future directions of research in the field. There is a special focus on Text Embedded with
Heterogeneous and Multimedia Data which makes the mining process much more challenging. A
number of methods have been designed such as transfer learning and cross-lingual mining for
such cases. Mining Text Data simplifies the content so that advanced-level students
practitioners and researchers in computer science can benefit from this book. Academic and
corporate libraries as well as ACM IEEE and Management Science focused on information
security electronic commerce databases data mining machine learning and statistics are the
primary buyers for this reference book.
