Research article Special Issues

Sentiment analysis for e-commerce product reviews by deep learning model of Bert-BiGRU-Softmax

  • Received: 03 July 2020 Accepted: 26 October 2020 Published: 09 November 2020
  • 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.

    Citation: Yi Liu, Jiahuan Lu, Jie Yang, Feng Mao. Sentiment analysis for e-commerce product reviews by deep learning model of Bert-BiGRU-Softmax[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 7819-7837. doi: 10.3934/mbe.2020398

    Related Papers:

  • 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.


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