Text Mining: Predictive Methods for Analyzing Unstructured Information

by Sholom Weiss, Tong Zhang, Nitin Indurkhya, et al.

Book Overview

Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our...

• Format: Hardcover
• Language: English
• ISBN: 0387954333
• ISBN13: 9780387954332
• Release Date: October 2004
• Publisher: Springer
• Length: 237 Pages
• Weight: 2.55 lbs.
• Dimensions: 0.8" x 6.5" x 9.3"

Customer Reviews

Great introduction book of text mining

This is a great book on text mining. It provides every detail you need to know to build a search engine or text analysis. There are several other books available on similar topics, but this one is definitely the best. Among all the chapters in this book, I like chapter 2 the best. It provides a complete list of solutions to convert the unstructured texts into vectors. Many researchers and enginners are familiar with the process, but few pay attention to many aspects as the book did, such as sentence boundary determination and phrase recognition. In one word, it is a great introduction book for someone new to the area, also a good handbook to check from time to time.

an idiot savant, statistical viewpoint

The authors give an excellent review of how matters stood in 2004, regarding text mining. The approach of the book is to minimise linguistic and semantic analysis. Instead, it looks more at the statistics of words (tokens) in documents. By using various such methods, they offer an automated way to classify documents. When this works, it can be a tremendous saver of manual effort. Think of the book as perhaps advocating an idiot savant vantage, and seeing how far one can usefully take this approach. The results of the methods can also be used as input to more advanced and specialised methods, that rely on semantic analysis. The book can also be applied to search engine analysis.

Very Good Intro Book

I wouldn't give it 5 stars, but definitely worth the money. I took an online class at statistics.com that used this as the text. Really a great combination of book and class and wasn't expensive. Highly recommend both to any data miner interested in getting into text mining.

An excellent introduction

I found the book informative and timely. The book describes the algorithms with psuedocode, and this made it possible for me to apply the algorithms to a legacy structure using another language within a few days of finishing the book. The book's software (available from their website) requires XML formatted documents for input.