This is the second part of a multi-part blog post in which I use various tools of Natural Language Processing to visualize Ulysses, by James Joyce. The aim is to provide proof in the collaborative possibilities between literary criticism and data science. The first part, where I elaborate on that idea as well as the inspiration for this project, can be found here. The code for the whole project can be found in its Github repository.

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Marilyn Monroe reading Ulysses

There are many, many ways to visualize a book. Even in the inspiration project for this post, there are four distinctive visualizations of the chapters using Euclidian, Manhattan, Canberra distances, and Normalized Compression Distance with mere TF-IDF values (more on this below).

#nlp #literature #data-science #james-joyce #philosophy #data analysis

Using NLP to Visualize Ulysses, Part Two
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