A decomposition of book structure through ousiometric fluctuations in cumulative word-time

TL;DR

This paper introduces a method using ousiometric fluctuations and empirical mode decomposition to analyze how the structure of books varies with length, revealing that longer texts are composed of oscillatory segments similar to concatenated shorter texts.

Contribution

The study presents a novel approach combining ousiometrics and empirical mode decomposition to analyze book structure through cumulative word-time, highlighting differences based on book length.

Findings

Longer books exhibit oscillatory fluctuations beyond general trends.

Fluctuation periods are typically a few thousand words, regardless of book length.

Longer texts resemble concatenations of shorter segments rather than expanded versions.

Abstract

While quantitative methods have been used to examine changes in word usage in books, studies have focused on overall trends, such as the shapes of narratives, which are independent of book length. We instead look at how words change over the course of a book as a function of the number of words, rather than the fraction of the book, completed at any given point; we define this measure as "cumulative word-time". Using ousiometrics, a reinterpretation of the valence-arousal-dominance framework of meaning obtained from semantic differentials, we convert text into time series of power and danger scores in cumulative word-time. Each time series is then decomposed using empirical mode decomposition into a sum of constituent oscillatory modes and a non-oscillatory trend. By comparing the decomposition of the original power and danger time series with those derived from shuffled text, we find that shorter books exhibit only a general trend, while longer books have fluctuations in addition to the general trend. These fluctuations typically have a period of a few thousand words regardless of the book length or library classification code, but vary depending on the content and structure of the book. Our findings suggest that, in the ousiometric sense, longer books are not expanded versions of shorter books, but are more similar in structure to a concatenation of shorter texts. Further, they are consistent with editorial practices that require longer texts to be broken down into sections, such as chapters. Our method also provides a data-driven denoising approach that works for texts of various lengths, in contrast to the more traditional approach of using large window sizes that may inadvertently smooth out relevant information, especially for shorter texts. These results open up avenues for future work in computational literary analysis, particularly the measurement of a basic unit of narrative.