Python 3 Text Processing with NLTK 3 Cookbook By Jacob Perkins

Key Features
Break text down into its component parts for spelling correction, feature extraction, and phrase transformation
Learn how to do custom sentiment analysis and named entity recognition
Work through the natural language processing concepts with simple and easy-to-follow programming recipes

What You Will Learn
Tokenize text into sentences, and sentences into words
Look up words in the WordNet dictionary
Apply spelling correction and word replacement
Access the built-in text corpora and create your own custom corpus
Tag words with parts of speech
Chunk phrases and recognize named entities
Grammatically transform phrases and chunks
Classify text and perform sentiment analysis

About
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.

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