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SA-Model: Multi-Feature Fusion Poetic Sentiment Analysis Based on a Hybrid Word Vector Model

Lingli Zhang1, Yadong Wu1,*, Qikai Chu2, Pan Li2, Guijuan Wang3,4, Weihan Zhang1, Yu Qiu1, Yi Li1

1 School of Computer Science and Engineering, Sichuan University of Science and Engineering, Zigong, 643000, China
2 School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, 643000, China
3 School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, 621000, China
4 School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621000, China

* Corresponding Author: Yadong Wu. Email: email

(This article belongs to the Special Issue: Recent Advances in Virtual Reality)

Computer Modeling in Engineering & Sciences 2023, 137(1), 631-645. https://doi.org/10.32604/cmes.2023.027179

Abstract

Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing, ancient literature research, etc. However, the existing research on sentiment analysis is relatively small. It does not effectively solve the problems such as the weak feature extraction ability of poetry text, which leads to the low performance of the model on sentiment analysis for Chinese classical poetry. In this research, we offer the SA-Model, a poetic sentiment analysis model. SA-Model firstly extracts text vector information and fuses it through Bidirectional encoder representation from transformers-Whole word masking-extension (BERT-wwm-ext) and Enhanced representation through knowledge integration (ERNIE) to enrich text vector information; Secondly, it incorporates numerous encoders to remove text features at multiple levels, thereby increasing text feature information, improving text semantics accuracy, and enhancing the model’s learning and generalization capabilities; finally, multi-feature fusion poetry sentiment analysis model is constructed. The feasibility and accuracy of the model are validated through the ancient poetry sentiment corpus. Compared with other baseline models, the experimental findings indicate that SA-Model may increase the accuracy of text semantics and hence improve the capability of poetry sentiment analysis.

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APA Style
Zhang, L., Wu, Y., Chu, Q., Li, P., Wang, G. et al. (2023). Sa-model: multi-feature fusion poetic sentiment analysis based on a hybrid word vector model. Computer Modeling in Engineering & Sciences, 137(1), 631-645. https://doi.org/10.32604/cmes.2023.027179
Vancouver Style
Zhang L, Wu Y, Chu Q, Li P, Wang G, Zhang W, et al. Sa-model: multi-feature fusion poetic sentiment analysis based on a hybrid word vector model. Comput Model Eng Sci. 2023;137(1):631-645 https://doi.org/10.32604/cmes.2023.027179
IEEE Style
L. Zhang et al., "SA-Model: Multi-Feature Fusion Poetic Sentiment Analysis Based on a Hybrid Word Vector Model," Comput. Model. Eng. Sci., vol. 137, no. 1, pp. 631-645. 2023. https://doi.org/10.32604/cmes.2023.027179



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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