This tutorial will be a hands on approach to learning natural language processing using NLTK, the Natural Language ToolKit. We will cover everything from tokenizing sentences to phrase extraction, from splitting words to training your own text classifiers for sentiment analysis. Please come prepared with NLTK already installed so we can dive into the code & data immediately.

Hour 1: Tokenization, Stemming & Corpora

Tokenization & familiarity with corpus readers and models are required knowledge before you can get into the more interesting aspects of NLTK. This first hour will include:

• an overview of modules & data
• loading pickled models
• sentence & word tokenization
• stemming & lemmatization
• an overview wordnet and other included corpora

Hour 2: Part-of-Speech Tagging & Chunking/NER

Using tokenization and a working knowledge of corpus readers & pickled models, we'll dive into part-of-speech tagging and chunking/NER, including:

• using a part-of-speech tagger
• an overview of tags and tagged corpora
• training a custom tagger with nltk-trainer
• using a chunker for phrase extraction and named entity recognition
• an overview of chunked corpora
• training a custom chunker with nltk-trainer

Hour 3: Text Classification & Sentiment Analysis

After using classifiers for training part-of-speech taggers and chunkers, this final hour will explain text classification in greater detail with:

• an overview of classified corpora
• text feature extraction
• an overview of classification algorithms & when to use them
• training a sentiment analysis classifier on movie reviews with nltk-trainer
• using a classifier for sentiment analysis
• hierarchical classification for sentiment analysis
• binary vs multi-label classification

Wrapping Up

Now that you know how to use NLTK to process some of the included English corpora, we'll wrap up by covering:

• non-english corpora included with NLTK
• other Python libraries for NLP
• custom corpus creation