Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit 1st Edition

NLTK was originally designed for teaching NLP, but because NLTK is so comprehensive, it is also quite vast, and you will need a guide to get you started on using it effectively. This book is that guide. Its organized into 4 sections. Chapters 1-4 cover the basics; 5-7 covers language processing, tagging, classification and information extraction; Chapters 8-10 covers sentence parsing, syntax, structure and representations of meaning, and Chapter 11 covers managing linguistics data. If you are looking to get an overview of NLP, as well as reasonable proficiency in manipulating text and extracting information from it, this book may be for you. People who are not NLP specialists but need to use NLP techniques at work will find this book particularly helpful. People who plan on specializing in NLP will probably find the book useful as a stepping stone into the field.My interest in NLP (and the book) is limited to being able to apply machine learning techniques to solve NLP problems, so I found the first two sections really useful. However, the entire book (including the exercises) is a great source of ideas on what you can accomplish in NLP with NLTK.

Buy this book only if you:1. Know the basics of natural language processing (NLP) or linguistics;2. Know the Python programming language or you're willing to learn it;3. Are using the NLTK library or plan to do so.NLTK is a Python library that offers many standard NLP tools (tokenizers, POS taggers, parsers, chunkers and others). It comes with samples of several dozens of text corpora typically used in NLP applications, as well as with interfaces to dictionary-like resources such as WordNet and VerbNet. No FrameNet, though. NLTK is well documented, so you might not need this book initially. However, it definitely helps to have it on your desk if you are serious about using NLTK.The first chapters are a bit messy, as they attempt to introduce all three themes (NLP, NLTK and Python) together. Beginners may have some difficulty sorting things out. By the time you reach the WordNet section, you either got lost in the forest, realize that you would never understand this topic without the book, or both. However, if you are a bit patient and try out all simple code examples, you'll make it eventually. In my opinion, NLTK remains the simplest, most elegant and well rounded library of its kind.

I'm only halfway through chapter 2 and I'm absolutely in love with this book. I originally bought it because I was interested in how to sort text you can find on the web (blogs, tweets, news articles) into categories, but after I started reading it I realized that it could also be used to create a tool that would help people to come up with rap / poetry more quickly (which is something I've daydreamed about since high school).The book is extremely well-written: a plain-English style which is very easy to digest and looks effortless but which I'm sure actually required a lot of thought (speaking from experience). The order in which they're introducing topics is brilliant IMO (again, speaking from experience of having to teach a complicated new topic).Highly recommended.

I am working on social network analysis and text mining. Of course, anyone can find the code pieces on the internet but this book provides a good framework for whoever wants to start working on text mining. The book is somewhat old (2009). For computer programming, even a year may make a book obsolete. However, the NLTK package is still one of the best beside other competitors like SpaCy. One important topic that the book does not cover is vectorization which is a relatively new topic. Overall, I enjoyed reading the book. It was well-organized and to the point for whatever you want to apply text mining to.

I have no prior experience or knowledge of Python but after reading through the first two chapters everything just clicked from then on out. Learning curve is about two chapters for those who have no previous experience programming in Python. Plenty of examples from the book and exercises that makes reading, learning, and programming fun. I actually bought this book for a computer science course even though there is a free online version, but having a paperback copy allows for easier look-ups of codes and examples when working on the exercises, and I can easily read it at my leisure. Very detail and well written, just follow along with the examples from the book and you can't go wrong. Easy to learn. Fun to program. Highly recommended.

Offers a good introduction to the topic, but could seriously do with an update. If you use this book, plan to devote about 15 minutes every hour to troubleshooting outdated code. It begins to get bad around the second half of chapter 2, and by chapter 3 there's an error with almost every other example code segment.