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Hi I wanted to know if there are some good books on text mining and classification with some case studies?. If not some papers/journals accessible to public would do. If they illustrate their examples with R even better. I am not looking for step by step manual but something which illustrates the pros and cons of various text mining approaches to various classes of problems.

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Check out http://lintool.github.com/MapReduceAlgorithms/MapReduce-book-final.pdf Data-Intensive Text Processing with MapReduce - this book is fairly academic but covers a number of commonly used text processing techniques and how they can be parrallised over large dataset using map reduce.

www.rtexttools.com This is an excellent R package which helps you to appply a wide range of classification algorithms (including some ensemble methods) to text analytics. and

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    $\begingroup$ To make this answer self-contained, would you mind providing a brief summary of each link? $\endgroup$ – chl Sep 3 '12 at 7:29
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I have recently read four books in this field:

Feldman, R. and James Sanger, J. (2006). The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press.

This one focuses on practical examples, software and applied text mining. It gives multiple examples of practical usage of text-mining. It could be of interest if you want to read about commercial applications of text-mining tools.

Srivastava, A.N. and Sahami, M. (2009). Text Mining: Classification, Clustering, and Applications. Chapman & Hall/CRC.

It is series of research papers that are used as examples of usage of different text-mining tools. It is rather too focused as for introductory test.

Weiss, S.M., Indurkhya, N., Zhang, T. and Damerau, F. (2005). Text Mining: Predictive Methods for Analyzing Unstructured Information. Springer.

Very introductory text that describes some general issues.

Manning, C. (1999). Foundations of Statistical Natural Language Processing. MIT Press.

This is the best book that I already read on this topic. It is well written, clear, goes deeper into theory but in practice-friendly way. Starts with general introduction, but than reviews some of the most commonly used methods and algorithms. If you would have to choose only a single book, I would recommend this one.

You could also easily find multiple books on natural language processing and text mining that focus on using R (tm library) or Python (nltk library).

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This might not be exactly on point for what you are looking for, but Mastering Regular Expressions by Jeffrey Friedl is a great source for learning how to use regular expressions to parse text. He doesn't discuss modeling techniques, but, armed with counts from applying regular expressions, you could apply a variety of standard modeling approaches.

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One book I go back to time and again for ideas is Text Mining: Predictive Methods... by Sholom Weiss. It has lots of ideas for approaching problems which I find useful since sometimes text mining is about trying different things - Global vs Local dictionary, number of features to keep, etc. I find this book to be a good idea generator. It also has case studies.

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I suggest NLP at http://www.nltk.org/ is free and couples with NLTK in python. all the best

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