depend on the use of basic natural language processing and information retrieval method-
ologies. This is particularly true of the sequence-centric approaches discussed in Chaps. 10
through 14 that are more closely related to natural language processing. Examples of an-
alytical methods that rely on natural language processing include information extraction,
event extraction, opinion mining, and text summarization, which frequently leverage basic
natural language processing tools like linguistic parsing or part-of-speech tagging. Needless
to say, natural language processing is a full fledged field in its own right (with excellent
books dedicated to it). Therefore, a question arises on how much discussion should be pro-
vided on techniques that lie on the interface of natural language processing and text mining
without deviating from the primary scope of this book. Our general principle in making
these choices has been to focus on mining and machine learning aspects. If a specific nat-
ural language or information retrieval method (e.g., part-of-speech tagging) is not directly
about text analytics, we have illustrated how to use such techniques (as black-boxes) rather
than discussing the internal algorithmic details of these methods. Basic techniques like part-
of-speech tagging have matured in algorithmic development, and have been commoditized
to the extent that many open-source tools are available with little difference in relative
performance. Therefore, we only provide working definitions of such concepts in the book,
and the primary focus will be on their utility as off-the-shelf tools in mining-centric settings.
The book provides pointers to the relevant books and open-source software in each chapter
in order to enable additional help to the student and practitioner.
The book is written for graduate students, researchers, and practitioners. The exposition
has been simplified to a large extent, so that a graduate student with a reasonable under-
standing of linear algebra and probability theory can understand the book easily. Numerous
exercises are available along with a solution manual to aid in classroom teaching.
Throughout this book, a vector or a multidimensional data point is annotated with a bar,
such as
X or y. A vector or multidimensional point may be denoted by either small letters
or capital letters, as long as it has a bar. Vector dot products are denoted by centered dots,
such as
X · Y . A matrix is denoted in capital letters without a bar, such as R. Throughout
the book, the n × d document-term matrix is denoted by D,withn documents and d
dimensions. The individual documents in D are therefore represented as d-dimensional row
vectors, which are the bag-of-words representations. On the other hand, vectors with one
component for each data point are usually n-dimensional column vectors. An example is
the n-dimensional column vector
y of class variables of n data points.
Yorktown Heights, NY, USA Charu C. Aggarwal
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