This book will show you the essential techniques of text and language processing. Starting with tokenization, stemming, and the WordNet dictionary, you'll progress to part-of-speech tagging, phrase chunking, and named entity recognition. You'll learn how various text corpora are organized, as well as how to create your own custom corpus. Then, you'll move onto text classification with a focus on sentiment analysis. And because NLP can be computationally expensive on large bodies of text, you'll try a few methods for distributed text processing. Finally, you'll be introduced to a number of other small but complementary Python libraries for text analysis, cleaning, and parsing.
This cookbook provides simple, straightforward examples so you can quickly learn text processing with Python and NLTK.
Comments about oreilly Python 3 Text Processing with NLTK 3 Cookbook:
My target was essentially to build a parser for a line based input. Instead I've read and tried all the code snippets (no problem, they all work out of the box). Not yet finished (a fair sized book), I'm impressed with the coverage and feel I could go much further with a linguistic bias.
I'm now looking at wrappers for nltk to go in the direction I want, knowing the basics are there.
Bottom Line Yes, I would recommend this to a friend