Open Access

ARTICLE

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

by Lingli Zhang¹, Yadong Wu^1,*, Qikai Chu², Pan Li², Guijuan Wang^3,4, 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:

(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

Received 19 October 2022; Accepted 11 January 2023; Issue published 23 April 2023

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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Keywords

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