<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>By reading and analyzing <italic>Moonlight Chopping</italic> and <italic>Cast Swords</italic> meticulously, this study reveals the polysemous textual world constituted by these two works, covering the mockery of dystopian realities, the inheritance of the spirit of vengeance, as well as the profound reverence for the essence of life. Web crawler technology is employed to obtain the textual content of <italic>Moonlight Chopping</italic> and <italic>Cast Swords</italic>, and the data is pre-processed. A corpus is constructed using a lexical algorithm. The in-depth extraction of thematic linguistic features of the two works is realized by combining Gibbs sampling and EM algorithm to accurately estimate the parameters of the LDA topic model. It is found that <italic>Moonlight Chopping</italic> and <italic>Cast Swords</italic> show significant correlation on the story and ideological level, but apparent differences in story focus, expression form and aesthetic style. Specifically, the average sentence length of <italic>Moonlight Chopping</italic> is only 1.18 units longer than <italic>Cast Swords</italic>. At the same time, its STTR value is only about 4.3% higher than <italic>Cast Swords</italic>, indicating that <italic>Moonlight Chopping</italic> has successfully achieved subtle thematic differences while maintaining the vengeful narrative style of <italic>Cast Swords</italic>, demonstrating a profound transformation of traditional culture and historical continuity.</p>
</abstract>

By reading and analyzing Moonlight Chopping and Cast Swords meticulously, this study reveals the polysemous textual world constituted by these two works, covering the mockery of dystopian realities, the inheritance of the spirit of vengeance, as well as the profound reverence for the essence of life. Web crawler technology is employed to obtain the textual content of Moonlight Chopping and Cast Swords, and the data is pre-processed. A corpus is constructed using a lexical algorithm. The in-depth extraction of thematic linguistic features of the two works is realized by combining Gibbs sampling and EM algorithm to accurately estimate the parameters of the LDA topic model. It is found that Moonlight Chopping and Cast Swords show significant correlation on the story and ideological level, but apparent differences in story focus, expression form and aesthetic style. Specifically, the average sentence length of Moonlight Chopping is only 1.18 units longer than Cast Swords. At the same time, its STTR value is only about 4.3% higher than Cast Swords, indicating that Moonlight Chopping has successfully achieved subtle thematic differences while maintaining the vengeful narrative style of Cast Swords, demonstrating a profound transformation of traditional culture and historical continuity.

Literary Criticism Based on the Theory of Information Dissemination-The Spiritual Recasting of <italic>Cast Swords</italic> in <italic>Moonlight Chopping</italic>

Center for Literary Theory of Henan University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Literary Criticism Based on the Theory of Information Dissemination-The Spiritual Recasting of  in 

By reading and analyzing Moonlight Chopping and Cast Swords meticulously, this study reveals the polysemous textual world constituted by these two works,...

Literary Criticism Based on the Theory of Information Dissemination-The Spiritual Recasting of Cast Swords in Moonlight Chopping

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 08 gen 2024

Accettato: 16 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0399

Parole chiave
Web crawlers, Disambiguation algorithm, LDA thematic model, Gibbs sampling, EM algorithm,

© 2024 Meng Lin, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Literary Criticism Based on the Theory of Information Dissemination-The Spiritual Recasting of Cast Swords in Moonlight Chopping

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 08 gen 2024

Accettato: 16 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0399

Parole chiaveWeb crawlers, Disambiguation algorithm, LDA thematic model, Gibbs sampling, EM algorithm,

© 2024 Meng Lin, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Web crawlers, Disambiguation algorithm, LDA thematic model, Gibbs sampling, EM algorithm,