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

<p>This paper explores how artificial Intelligence enhances emotional expression and aesthetic imagery in modern Chinese literature. The study focuses on the latent semantic analysis of literary texts, text mining challenges, techniques and algorithms for latent semantic analysis, and the application of artificial Intelligence in literary criticism. Through text representation, analysis of text mining challenges, latent semantic analysis (LSA), and the application of specific algorithms, this study provides an in-depth understanding of the intrinsic semantics of literary texts. The study employs text representation methods, such as the TF-IDF formulation, to deal with high-dimensional sparse text data. The main challenges include high dimensionality and discovering potential semantic relationships between words. Latent semantic analysis (LSA) realizes data dimensionality reduction through SVD singular value decomposition technique to extract the implicit relationships between words in text. The results show that LSA effectively pulls the potential semantic structure of the text, reduces the “noise”, simplifies the text vectors, and realizes the dimensionality reduction. In addition, the text clustering analysis using Kohonen self-organized feature mapping network reveals the semantic relationship and sentiment distribution among literary works. Intelligent technology can effectively enhance literary works’ emotional depth and aesthetic value and provide a new interpretation perspective for modern literature.</p>
</abstract>

This paper explores how artificial Intelligence enhances emotional expression and aesthetic imagery in modern Chinese literature. The study focuses on the latent semantic analysis of literary texts, text mining challenges, techniques and algorithms for latent semantic analysis, and the application of artificial Intelligence in literary criticism. Through text representation, analysis of text mining challenges, latent semantic analysis (LSA), and the application of specific algorithms, this study provides an in-depth understanding of the intrinsic semantics of literary texts. The study employs text representation methods, such as the TF-IDF formulation, to deal with high-dimensional sparse text data. The main challenges include high dimensionality and discovering potential semantic relationships between words. Latent semantic analysis (LSA) realizes data dimensionality reduction through SVD singular value decomposition technique to extract the implicit relationships between words in text. The results show that LSA effectively pulls the potential semantic structure of the text, reduces the “noise”, simplifies the text vectors, and realizes the dimensionality reduction. In addition, the text clustering analysis using Kohonen self-organized feature mapping network reveals the semantic relationship and sentiment distribution among literary works. Intelligent technology can effectively enhance literary works’ emotional depth and aesthetic value and provide a new interpretation perspective for modern literature.

Artificial Intelligence Empowers Emotional Expression and Aesthetic Imagery in Modern Chinese Literature

Henan Vocational University of Science and Technology

Applied Mathematics and Nonlinear Sciences

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

This paper explores how artificial Intelligence enhances emotional expression and aesthetic imagery in modern Chinese literature. The study focuses on the...

Artificial Intelligence Empowers Emotional Expression and Aesthetic Imagery in Modern Chinese Literature

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 17 sty 2024

Przyjęty: 23 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0659

Słowa kluczowe
Mining puzzle analysis, Specific algorithm, Data dimensionality reduction, Text clustering analysis

© 2024 Kaibo Jiang, published by Sciendo

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

Artificial Intelligence Empowers Emotional Expression and Aesthetic Imagery in Modern Chinese Literature

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 17 sty 2024

Przyjęty: 23 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0659

Słowa kluczoweMining puzzle analysis, Specific algorithm, Data dimensionality reduction, Text clustering analysis

© 2024 Kaibo Jiang, published by Sciendo

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

Słowa kluczowe
Mining puzzle analysis, Specific algorithm, Data dimensionality reduction, Text clustering analysis