Thanks to projects like this year’s Panama Papers, text mining applications are gaining more visibility for their ability to draw insight from the unstructured content that makes up the majority of today’s big data. If you’re looking to go deeper into the topic, the following list of text mining books come highly recommended from the Expert System team. Obviously, this is just a sample of what’s out there, so we invite you to share your feedback and suggestions for other text mining books in the comments below.

the 5 must read text mining books:

  1. Deep Text: Using Text Analytics to Conquer Information Overload, Get Real Value from Social Media, and Add Bigger Text to Big Data by Tom Reamy, published by Information Today Inc. (available July 2016) . I have known Tom for many years and can attest to his expertise in working on real use cases that generate business value for organizations. In his book, Tom pulls from these experiences to share some of his most challenging cases. Whether you’re interested in gaining insight from a stream of social media content or making use of a huge amount of business information, Deep Text is the text mining book that provides tested insights and examples for doing this effectively.
  1. Introduction to Information Retrieval by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, published by Cambridge University Press. If I had to recommend an introductory text mining book, this is the one. This book, which is also used by the Stanford University program, is a comprehensive manual that provides a great overview of text mining, explains all the terminology and still manages to generate the interest to learn even more. While it’s not a book for business readers, it’s a great resource for helping your technical team grasp the basics.
  1. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data by Ronen Feldman and James Sanger, published by Cambridge University Press. This is the text mining book to turn to if you’re looking for practical examples, software and applied text mining. The concrete text mining examples alone make it a valuable resource for business readers.
  1. Text Mining: Predictive Methods for Analyzing Unstructured Information by Sholom M. Weiss, Nitin Indurkhya, Tong Zhang and Fred J. Damerau, published by Springer. Among text mining books, this is a great book for people who want to learn. It does a great job of introducing the most common issues faced by anyone hen dealing with text mining problems.
  1. Foundations of Statistical Natural Language Processing by Christopher D. Manning and Hinrich Schütze, published by MIT Press. This is a comprehensive, well written, and clear text mining book that provides lots of detail on theory in a way that is easily understood by the non-expert. After a general introduction, it covers the most commonly used methods and algorithms. Like no any other text mining books, this is the book that you want to read if you are not a pure business person who wants to grasp the economic value of text mining..

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