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Product details
File Size: 2302 KB
Print Length: 414 pages
Publisher: Apress; 1st ed. edition (November 30, 2016)
Publication Date: November 30, 2016
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B01NBCI1HF
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Amazon Best Sellers Rank:
#469,270 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
One of my go-to books for natural language processing with Python has been Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. This has been the book for me and was one of my dissertation references. I used this book so much, that I I had to buy a second copy of this book because I wore the first one out. I’ve read many other NLP books but haven’t found any that could match this book – till now.This is a great book and has now taken a permanent place on my bookshelf.Unlike many books that I run across, this book spends plenty of time talking about the theory behind things rather than just doing some hand-waving and then showing some code. In fact, there isn’t any code (that I saw) until page 41. That’s impressive these days. Here’s a quick overview of the book’s layout:Chapter 1 provides the baseline for Natural Language. This is a very good overview for anyone that’s never worked much with NLP.Chapter 2 is a python ‘refresher’. If you don’t know python at all but know some other language, this should get you started enough to use the rest of the book.Chapter’s 3 – 7 is there the real fun begins. These chapters cover Text Classification, Summarization Similarity / Clustering and Semantic / Sentiment Analysis.If you have some familiarity with python and NLP, you can jump to Chapter 3 and dive into the details.What I really like about this book is that it places theory first. I’m a big fan of ‘learning by doing’ but I think before you can ‘do’ you need to know ‘why’ you are doing what you are doing. The code in the book is really well done as well and uses the NLTK, Sklearn and gensim libraries for most of the work. Additionally, there are multiple ‘build your own’ sections where the author provides a very good overview (and walk-through) of what it takes to build your own functionality for your own NLP work.
The author goes through a number of very useful topics, and having already been trained in NLP and semantic analysis this was a nice overview with detailed code. I have not tested all the classes and functions but the flow of the book seems right for a crash course in what to do with million reviews in one week!
Nice, concise discussion of several hot topics of Text Analytics. I recommend for new beginners.
Good examples with some tools not included in other cookbooks ( like space)
Great into chapter on linguistics and code that you can download and be up and running immediately. Well written as well. For me, it's a fascinating topic, and properly addressed in this text.
Excellent book. I'm new to text mining and felt some of the chapters got me up to speed immediately
First half of book is terminology and how language works. Not enough focus on implementing projects
Very comprehensive coverage of almost all aspects of traditional Natural Language Processing (NLP), contains tons of code using pure Python as well as using NLTK, pattern, gensim and SpaCy libraries that can be extended or reused for your own projects. As another reviewer has noted, this is an excellent replacement (slightly less deep but much broader coverage) for Bird, et al's Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. If I had a complaint, it is that the book tends to go a little heavy on theory, since its stated target audience is NLP practitioners. One other thing is that there is zero coverage of Deep Learning, although that wasn't something I was looking for when reading this book. Overall, I thought that this was an excellent and broad introduction to traditional NLP. It will get you started with reusable code snippets, and will give you (more than) enough theoretical understanding so you can consult more advanced books or papers as needed.
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