Characterizing narrative time in books through fluctuations in power and danger arcs

要約

タイトル：『Characterizing narrative time in books through fluctuations in power and danger arcs』

要約：
– 本論文では、本の長さに依存しない物語の形状などの一般的なトレンドではなく、本の進行状況に応じて単語がどのように変化するかに着目する。
– 『累積単語時』という尺度を導入し、意味論的差別力から得られたvalence-arousal-dominanceフレームワークの再解釈であるousiometricsを用いて、テキストをパワーとデンジャースコアの時間系列データに変換する。
– これにより、本の長さにかかわらず、書籍のコンテンツと構造によって変化する、数千単語の周期を持つ波動的なモードが得られる。
– 本研究の成果は、よりデータ駆動的な文章の解析手法を提供し、特に物語の基本単位の測定についての今後の研究に可能性を開く。

要約(オリジナル)

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.

arxiv情報

提供元, 利用サービス

arxiv.jp, OpenAI