Management School, Hangzhou Dianzi University, Hangzhou, 310018, China.
Math Biosci Eng. 2020 Nov 9;17(6):7819-7837. doi: 10.3934/mbe.2020398.
Sentiment analysis of e-commerce reviews is the hot topic in the e-commerce product quality management, from which manufacturers are able to learn the public sentiment about products being sold on e-commerce websites. Meanwhile, customers can know other people's attitudes about the same products. This paper proposes the deep learning model of Bert-BiGRU-Softmax with hybrid masking, review extraction and attention mechanism, which applies sentiment Bert model as the input layer to extract multi-dimensional product feature from e-commerce reviews, Bidirectional GRU model as the hidden layer to obtain semantic codes and calculate sentiment weights of reviews, and Softmax with attention mechanism as the output layer to classify the positive or negative nuance. A series of experiments are conducted on the large-scale dataset involving over 500 thousand product reviews. The results show that the proposed model outperforms the other deep learning models, including RNN, BiGRU, and Bert-BiLSTM, which can reach over 95.5% of accuracy and retain a lower loss for the e-commerce reviews.
电商评论的情感分析是电商产品质量管理中的热门话题，制造商从中可以了解公众对电商网站上销售产品的看法。同时，消费者也可以了解其他人对同一产品的态度。本文提出了一种基于混合掩蔽、评论提取和注意力机制的 Bert-BiGRU-Softmax 深度学习模型，将情感 Bert 模型作为输入层，从电商评论中提取多维产品特征；将双向 GRU 模型作为隐藏层，获取评论的语义编码和情感权重；将带有注意力机制的 Softmax 作为输出层，对评论的正负面细微差别进行分类。在涉及超过 50 万条产品评论的大规模数据集上进行了一系列实验。结果表明，所提出的模型优于其他深度学习模型，包括 RNN、BiGRU 和 Bert-BiLSTM，其准确率超过 95.5%，并且对电商评论的损失较低。
