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

<p>This paper first analyzes the emotional features of English and American literary works by combining semiotics of interactivity, language class emotional context and thinking operation model. Then, a B-Feature-BP text emotion feature construction model is constructed on the basis of the BRET model combined with the BP neural network, and the emotion features of English and American literary works are constructed by combining semiotics and the thought operation model. Then, based on the multi-task learning method in deep learning, a multi-task MT-GSU model is proposed to classify and recognize the emotional features of constructed text. Finally, the performance of constructing, classifying and recognizing the emotional features of English and American literary works in this paper is analyzed so as to analyze the emotional features of English and American literary works. The results show that the performance of the constructed emotional features is all greater than 0.8, and the emotional features of the characters, the environment, and the whole of the English and American literary works are above 0.87, and the classification time is between [0.338,0.721]s. The intensity of tendency of the characteristics of the emotions of the works of English and American literature is [0.68,0.78], the intensity of stability is [0.6,0.74], the intensity of profundity is [0.71,0.79], and the intensity of efficacy is [0.72,0.82]. This study has a positive impact on the appreciation and translation of English and American literary works.</p>
</abstract>

This paper first analyzes the emotional features of English and American literary works by combining semiotics of interactivity, language class emotional context and thinking operation model. Then, a B-Feature-BP text emotion feature construction model is constructed on the basis of the BRET model combined with the BP neural network, and the emotion features of English and American literary works are constructed by combining semiotics and the thought operation model. Then, based on the multi-task learning method in deep learning, a multi-task MT-GSU model is proposed to classify and recognize the emotional features of constructed text. Finally, the performance of constructing, classifying and recognizing the emotional features of English and American literary works in this paper is analyzed so as to analyze the emotional features of English and American literary works. The results show that the performance of the constructed emotional features is all greater than 0.8, and the emotional features of the characters, the environment, and the whole of the English and American literary works are above 0.87, and the classification time is between [0.338,0.721]s. The intensity of tendency of the characteristics of the emotions of the works of English and American literature is [0.68,0.78], the intensity of stability is [0.6,0.74], the intensity of profundity is [0.71,0.79], and the intensity of efficacy is [0.72,0.82]. This study has a positive impact on the appreciation and translation of English and American literary works.

Analyzing the Emotional Characteristics of British and American Literature by Combining Semiotics and the Operational Model of Thinking

Applied Mathematics and Nonlinear Sciences

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

This paper first analyzes the emotional features of English and American literary works by combining semiotics of interactivity, language class emotional...

Analyzing the Emotional Characteristics of British and American Literature by Combining Semiotics and the Operational Model of Thinking

Publicado en línea: 06 dic 2023

Páginas: -

Recibido: 07 feb 2023

Aceptado: 16 jun 2023

DOI: https://doi.org/10.2478/amns.2023.2.01387

Palabras clave
BP neural network, B-Feature-BP feature construction, Deep learning, MT-GSU model, Textual sentiment features

© 2023 Ning Huang, published by Sciendo

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

Analyzing the Emotional Characteristics of British and American Literature by Combining Semiotics and the Operational Model of Thinking

Publicado en línea: 06 dic 2023

Páginas: -

Recibido: 07 feb 2023

Aceptado: 16 jun 2023

DOI: https://doi.org/10.2478/amns.2023.2.01387

Palabras claveBP neural network, B-Feature-BP feature construction, Deep learning, MT-GSU model, Textual sentiment features

© 2023 Ning Huang, published by Sciendo

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

Palabras clave
BP neural network, B-Feature-BP feature construction, Deep learning, MT-GSU model, Textual sentiment features