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

作     者：Lingli Zhang Yadong Wu Qikai Chu Pan Li Guijuan Wang Weihan Zhang Yu Qiu Yi Li 

作者机构：School of Computer Science and EngineeringSichuan University of Science and EngineeringZigong643000China School of Automation and Information EngineeringSichuan University of Science and EngineeringZigong643000China School of Computer Science and TechnologySouthwest University of Science and TechnologyMianyang621000China School of Information EngineeringSouthwest University of Science and TechnologyMianyang621000China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2023年第137卷第10期

页      面：631-645页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Sentiment analysis Chinese classical poetry natural language processing BERT-wwm-ext ERNIE multi-feature fusion 

摘      要：Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing,ancient literature research,***,the existing research on sentiment analysis is relatively *** 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 *** this research,we offer the SA-Model,a poetic sentiment analysis ***-Model firstly extracts text vector information and fuses it through Bidirectional encoder representation from transformers-Whole word masking-extension(BERT-wwmext)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 *** feasibility and accuracy of the model are validated through the ancient poetry sentiment *** 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.