基于中文BERT和融合深度神经网络的句子级中文电子商务产品评论情感分析

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS
Hong Fang, Guangjie Jiang, Desheng Li
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引用次数: 0

摘要

在互联网快速发展的推动下,电子商务平台上出现了更多的电子商务产品评论,这些评论可以帮助企业做出商业决策。目前,在英语文本情感分析(SA)中,嵌入层中使用的双向变换编码器表示(BERT)方法取得了很好的效果。本文提出了一种基于融合深度神经网络的中文BERT模型(CBERT-FDNN),该模型能够从中文文本中提取更丰富、更准确的语义和语法信息。首先,在嵌入层使用全词掩模中文BERT (Chinese-BERT-wwm)生成动态句子表示向量;它是一种基于全词掩蔽(WWM)技术的中文预训练模型,对中文文本上下文嵌入更为有效。其次,设计多通道多尺度卷积神经网络(CNN)和双向长短期记忆(BiLSTM),在特征提取层进一步捕获关键特征。为了获得更全面的句子属性,这些特征被连接在一起。最后,对10万条句子级中文电子商务产品评论进行情感二分类。准确率和F1分数分别达到94.37%和94.34%。与基线模型相比,实验表明该模型具有更高的精度和更好的预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sentiment analysis based on Chinese BERT and fused deep neural networks for sentence-level Chinese e-commerce product reviews
Driven by the rapid development of Internet, more e-commerce product reviews are available on e-commerce platforms, which can help enterprises make business decisions. Currently, bidirectional encoder representations from transformers (BERT) applied in the embedding layer contributes to achieve promising results in English text sentiment analysis (SA). This paper proposes a novel model Chinese BERT with fused deep neural networks (CBERT-FDNN), extracting richer and more accurate semantic and grammatical information in Chinese text. First, Chinese BERT with whole word masking (Chinese-BERT-wwm) is used in the embedding layer to generate dynamic sentence representation vectors. It is a Chinese pre-training model based on the whole word masking (WWM) technology, which is more effective for Chinese text contextual embedding. Second, multi-channel and multi-scale convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) are designed to capture further crucial features in the feature extraction layer. To obtain more comprehensive sentence attributes, these features are concatenated together. Last, the model is evaluated on 100,000 sentence-level Chinese e-commerce product reviews for sentiment binary classification. The accuracy and F1 score can achieve 94.37% and 94.34%, respectively. Compared with the baseline models, the experiments show that our proposed model has higher accuracy and better prediction performance.
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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