TEXT DATA MANAGEMENT AND ANALYSIS

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.

REVIEWS

In general terms, the authors typically provide verbose descriptions of the reasons behind the design of specific techniques, with numerical examples and illustrative figures from the slides of two massive open online courses (MOOCs) offered by the first author on Coursera. They also provide specific sections that describe in detail the proper way to evaluate every different kind of technique, a key factor to be taken into account when applying the discussed techniques in practice.

The book, however, is not always self-contained, since its broad scope in a limited number of pages entails an unavoidable depth/breadth tradeoff. Most basic techniques can be implemented just by following the instructions and guidelines in the text, although interested readers might need to resort to the bibliographic references if they want to gain a thorough understanding of the many advanced techniques. Fortunately, the authors include some bibliographic notes and very selective suggestions for further reading at the end of each chapter, instead of the encyclopedic collection of references common in many other textbooks.

Although readers will not find detailed coverage of NLP techniques and some chapters might seem lacking in depth, advanced undergraduate students might find this book to be a valuable reference for getting acquainted with both information retrieval and text mining in a single volume, a worthwhile achievement for a 500-page textbook.
Fernando Berzal – In “Computing Reviews”

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