Customer Reviews

Text Mining: Predictive Methods for Analyzing Unstructured Information

5 star:		(3)
4 star:		(1)
3 star:		(0)
2 star:		(0)
1 star:		(1)

 

 

The authors (4 guys) tried to cover all IR big words as much as they can, and ended up with the 221 pages book. Let's take one example, inverted index takes 1.5 pages: It says inverted index is a table with the key-pair. The key is all the keywords scanned from the source, and the value is the document and word position (key in that document), period. IMHO, it is apparent facts that inverted index is like this way. But in practical world algorithm, it is much more complicated than a table, for example, how to incremental fill the index, how to sync between multiple backup copy, how to blabla. And even THAT google paper is more useful than it on this area. People may argue it is a comprehensive introduction book, well, then try Gerald's classic book. The whole impression is like I am reading a C++ programming book which spends 10 pages talking K&R's from Bell, how long they had been there, etc ...
I spent about 1 hr scanning the whole book without much left on my brain . Considering the price 69 bucks, I have to give it 0 on performance/price.

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.

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.

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.

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.