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Text Data Management and Analysis - A Practical Introduction to Information Retrieval and Text Mining (Paperback)
Loot Price: R2,532
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Text Data Management and Analysis - A Practical Introduction to Information Retrieval and Text Mining (Paperback)
Series: ACM Books
Expected to ship within 10 - 15 working days
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Recent years have seen a dramatic growth of natural language text
data, including web pages, news articles, scientific literature,
emails, enterprise documents, and social media such as blog
articles, forum posts, product reviews, and tweets. This has led to
an increasing demand for powerful software tools to help people
analyze and manage vast amounts of text data effectively and
efficiently. Unlike data generated by a computer system or sensors,
text data are usually generated directly by humans, and are
accompanied by semantically rich content. As such, text data are
especially valuable for discovering knowledge about human opinions
and preferences, in addition to many other kinds of knowledge that
we encode in text. In contrast to structured data, which conform to
well-defined schemas (thus are relatively easy for computers to
handle), text has less explicit structure, requiring computer
processing toward understanding of the content encoded in text. The
current technology of natural language processing has not yet
reached a point to enable a computer to precisely understand
natural language text, but a wide range of statistical and
heuristic approaches to analysis and management of text data have
been developed over the past few decades. They are usually very
robust and can be applied to analyze and manage text data in any
natural language, and about any topic.This book provides a
systematic introduction to all these approaches, with an emphasis
on covering the most useful knowledge and skills required to build
a variety of practically useful text information systems. The focus
is on text mining applications that can help users analyze patterns
in text data to extract and reveal useful knowledge. Information
retrieval systems, including search engines and recommender
systems, are also covered as supporting technology for text mining
applications. The book covers the major concepts, techniques, and
ideas in text data mining and information retrieval from a
practical viewpoint, and includes many hands-on exercises designed
with a companion software toolkit (i.e., MeTA) to help readers
learn how to apply techniques of text mining and information
retrieval to real-world text data and how to experiment with and
improve some of the algorithms for interesting application tasks.
The book can be used as a textbook for a computer science
undergraduate course or a reference book for practitioners working
on relevant problems in analyzing and managing text data.
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