MSGAT-Based Sentiment Analysis for E-Commerce

Inf. Comput. Pub Date : 2023-07-19 DOI:10.3390/info14070416
Tingyao Jiang, Wei Sun, Min Wang
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Abstract

Sentence-level sentiment analysis, as a research direction in natural language processing, has been widely used in various fields. In order to address the problem that syntactic features were neglected in previous studies on sentence-level sentiment analysis, a multiscale graph attention network (MSGAT) sentiment analysis model based on dependent syntax is proposed. The model adopts RoBERTa_WWM as the text encoding layer, generates graphs on the basis of syntactic dependency trees, and obtains sentence sentiment features at different scales for text classification through multilevel graph attention network. Compared with the existing mainstream text sentiment analysis models, the proposed model achieves better performance on both a hotel review dataset and a takeaway review dataset, with 94.8% and 93.7% accuracy and 96.2% and 90.4% F1 score, respectively. The results demonstrate the superiority and effectiveness of the model in Chinese sentence sentiment analysis.
基于msgat的电子商务情感分析
句子级情感分析作为自然语言处理的一个研究方向,已广泛应用于各个领域。针对以往在句子级情感分析研究中忽略句法特征的问题,提出了一种基于依赖句法的多尺度图注意网络(MSGAT)情感分析模型。该模型采用RoBERTa_WWM作为文本编码层,在句法依赖树的基础上生成图,并通过多层图关注网络获得不同尺度的句子情感特征,用于文本分类。与现有主流文本情感分析模型相比,本文提出的模型在酒店点评数据集和外卖点评数据集上都取得了更好的性能,准确率分别为94.8%和93.7%,F1得分分别为96.2%和90.4%。结果表明了该模型在汉语句子情感分析中的优越性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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