Visualizing Lexical Novelty in Literature

Novels are full of new characters, new locations and new expressions. The discourse between characters involves new ideas being exchanged. We can get a hint of this by tracking the introduction of new terms in a novel. In the below visualizations (in which each column represents a chapter and each small block a paragraph of text), I maintain a variable which represents novelty. When a paragraph contains more than 25% new terms (i.e. words that have not been observed thus far) this variable is set at its maximum of 1.

Novels are full of new characters, new locations and new expressions. The discourse between characters involves new ideas being exchanged. We can get a hint of this by tracking the introduction of new terms in a novel. In the below visualizations (in which each column represents a chapter and each small block a paragraph of text), I maintain a variable which represents novelty. When a paragraph contains more than 25% new terms (i.e. words that have not been observed thus far) this variable is set at its maximum of 1. Otherwise, the variable decays. The variable is used to colour the paragraph with red being 1.0 and blue being 0. The result is that we can get an idea of the introduction of new ideas in novels.

In the first book – Austen’s Sense and Sensibility – we can see two things. Firstly, the start of the book keeps a pretty good degree of novelty for the first few chapters. Secondly, each chapter introduces something new.

The second book – Stevenson’s Kidnapped – shows a different pattern. While it starts off with reasonable novelty, this then dies out for most of the book with spurts of interest here and there.

What is surprising to me (if we take any real meaning from this approach) is that Austen’s Emma – the third book – is strong out of the gate (the first 18 chapters) but fails to break the 25% novelty ceiling thereafter.

[Note that these results are preliminary and I’m going to do more validation and testing.]

Update: see below the original visualization for an updated version with more accurate results.

 
Update: After looking at the above results I drilled down on the strange behaviour in Emma. It turns out that Emma as multiple volumes within which the chapter counter reset to I. Consequently I was picking up chapter titles (I, II, III, VI, V, etc.) as novel terms the first go round and this was driving the visualization. I’ve since modified the algorithm to firstly ignore text blocks (paragraphs) with fewer than 5 words and secondly, given it a more dynamic colour scheme.

This improvement still highlights some key differences (again, in as much as the algorithm is correct). However, these differences are now somewhat changed from the first set of observations. Note also that the threshold for novelty has been decreased to 0.1.