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<p>With the development of the times, the cultivation of literary appreciation ability in the education system of colleges and universities has been gradually emphasized. This paper constructs a multimodal semantic analysis model based on text feature extraction, image feature extraction and audio feature extraction, and after fusing multiple modalities, combines them with sentiment semantic Analysis, optimizes them through the self-attention mechanism module, and finally constructs a sentiment semantic analysis model based on multimodal feature fusion. Through the performance analysis of the multimodal feature emotion semantic model, the algorithm model accuracy of this paper is as high as 89%, and the recognition accuracy of emotion is stable at about 80~85%. The multimodal sentiment semantic model proposed in this paper has an accuracy rate higher than 80% on literature appreciation related datasets. In the empirical evidence of literature appreciation, the average recognition accuracy rate of emotions expressed in literature is 62.80%. The study provides an achievable path for the multimodal semantic Analysis of contemporary literature appreciation integrated into college teaching, which is of practical significance for developing literature appreciation teaching in colleges and universities.</p>
</abstract>

With the development of the times, the cultivation of literary appreciation ability in the education system of colleges and universities has been gradually emphasized. This paper constructs a multimodal semantic analysis model based on text feature extraction, image feature extraction and audio feature extraction, and after fusing multiple modalities, combines them with sentiment semantic Analysis, optimizes them through the self-attention mechanism module, and finally constructs a sentiment semantic analysis model based on multimodal feature fusion. Through the performance analysis of the multimodal feature emotion semantic model, the algorithm model accuracy of this paper is as high as 89%, and the recognition accuracy of emotion is stable at about 80~85%. The multimodal sentiment semantic model proposed in this paper has an accuracy rate higher than 80% on literature appreciation related datasets. In the empirical evidence of literature appreciation, the average recognition accuracy rate of emotions expressed in literature is 62.80%. The study provides an achievable path for the multimodal semantic Analysis of contemporary literature appreciation integrated into college teaching, which is of practical significance for developing literature appreciation teaching in colleges and universities.

A Multimodal Semantic Analysis of the Integration of Contemporary Literary Appreciation into College Teaching and Learning

Applied Mathematics and Nonlinear Sciences

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

With the development of the times, the cultivation of literary appreciation ability in the education system of colleges and universities has been gradually...

A Multimodal Semantic Analysis of the Integration of Contemporary Literary Appreciation into College Teaching and Learning

Publié en ligne: 26 févr. 2024

Pages: -

Reçu: 04 janv. 2024

Accepté: 11 janv. 2024

DOI: https://doi.org/10.2478/amns-2024-0374

Mots clés
Feature extraction, Multimodal features, Self-attention mechanism, Sentiment semantic Analysis, Literature appreciation

© 2024 Huiling Liu, published by Sciendo

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

A Multimodal Semantic Analysis of the Integration of Contemporary Literary Appreciation into College Teaching and Learning

Publié en ligne: 26 févr. 2024

Pages: -

Reçu: 04 janv. 2024

Accepté: 11 janv. 2024

DOI: https://doi.org/10.2478/amns-2024-0374

Mots clésFeature extraction, Multimodal features, Self-attention mechanism, Sentiment semantic Analysis, Literature appreciation

© 2024 Huiling Liu, published by Sciendo

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

Mots clés
Feature extraction, Multimodal features, Self-attention mechanism, Sentiment semantic Analysis, Literature appreciation