Deep Text: Using Text Analytics to Conquer Information Overload, Get Real Value from Social Media, and Add Big(ger) Text to Big Data
| DOI | https://doi.org/10.1108/EL-09-2017-0188 |
| Date | 06 November 2017 |
| Pages | 1269-1270 |
| Published date | 06 November 2017 |
| Author | Behrooz Bayat |
| Subject Matter | Information & knowledge management,Information & communications technology,Internet |
but does not need to be read from cover to cover. For those actively engaged in the field, it is a
valuable resource.
Raewyn Adams
Clinical School Library, Bay of Plenty District Health Board, Tauranga, New Zealand
Deep Text: Using Text Analytics to Conquer Information Overload,
Get Real Value from Social Media, and Add Big(ger) Text to Big Data
by Tom Reamy
Medford, NJ
Information Today
2016
424 p.
US$59.50
Soft cover
ISBN: 978-1-57387-529-5
Review DOI 10.1108/EL-09-2017-0188
Deep text is an approach to text analytics that involves using computerized techniques for
gaining insights into large volumes of unstructured text. This book looks in depth at what text
analytics is and how it can be practiced in a way that goes beyond text mining. It describes the
nature of text analytics generally and the vital role that a deep text approach can play in
making text analytics successful. The book gives an understanding of text analytics and how it
can be carried out, and also the kinds of applications text analytics can support. The context is
usually the corporate sector.
Thebookisdividedintofive parts each including three chapters. The first part, “Text
Analytics Basics”, lays the foundations for text analytics by providing a general picture of the
concept. Chapter 1 presents a broad definition of text analytics, what it involves and what it can
provide. This chapter also discusses the importance of content models and metadata in adding
structure to unstructured texts and briefly describes the technology behind text analytics.
Chapter 2 looks at the major core capability areas within text analytics including text mining,
extraction, summarization, sentiment analysis and auto-categorization, all of which require the
design of difficult and often expensive software. Chapter 3 considers the important issue of the
return on investment of text analytics and analyzes the basic business logic of text analytics,
which is to add structure to an enormous amount of unstructured text and to get value from it.
The chapter also describes three major areas in which text analytics appears significantly
beneficial to an organization including enterprise, search, social media and multiple text analytics.
The second part of the book, “Getting Started in Text Analytics”, suggests that getting
familiar with the available text analytics software in the market and researching the
information environment and company needs are necessary first steps to be taken. To this
end, this part includes chapters on thecurrent state of text analytics software, a smart start
to text analytics and the evaluationof text analytics software.
The third part describes the process to go through to implement text analytics applications.
Therefore, Chapter 7 looks at the issue of developing auto-categorization as the most
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