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.
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