Conclusion

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.
“Python and NLTK FAQs”, resources compiled by Na-Rae Han.

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