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At this point, you should have a familiarity with what is possible with text analysis, and some of the most important functions (i.e., cleaning and part-of-speech tagging). Yet, this tutorial has only scratched the surface of what is possible with text analysis and natural language processing. It is a rapidly growing field, if you are interested, be sure to work through the online NLTK Book.
More Resources:
- NLTK Documentation
- FreeLing, an open source language analysis tool suite.
- "Sentiment Analysis for Exploratory Data Analysis", a lesson to conduct ‘sentiment analysis’ on texts with Python and NLTK.
- Journal of Digital Humanities, Vol. 2, No. 1 Winter 2012 contains interesting several interesting articles about topic modeling and text analysis.
- Classical Language Toolkit on GitHub, Natural Language Processing specifically for Classical languages.
- "A Bossy Sort of Voice", describes a Python/NLTK project quantifying gender bias in Harry Pottery with Python and NLTK.
- "Finding Patterns in Gothic Literature", describes a Python/NLTK project analyzing color in Gothic Literature.