Sentiment analysis for e-commerce product reviews in Chinese based on sentiment lexicon and deep learning

Yang, L., Li, Y., Wang, J. and Sherratt, S. ORCID: https://orcid.org/0000-0001-7899-4445 (2020) Sentiment analysis for e-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE Access, 8. pp. 23522-23530. ISSN 2169-3536 doi: 10.1109/ACCESS.2020.2969854

Abstract/Summary

In recent years, with the rapid development of Internet technology, online shopping has become a mainstream way for users to purchase and consume. Sentiment analysis of a large number of user reviews on e-commerce platforms can effectively improve user satisfaction. This paper proposes a new sentiment analysis model-SLCABG, which is based on the sentiment lexicon and combines Convolutional Neural Network (CNN) and attention-based Bidirectional Gated Recurrent Unit (BiGRU). In terms of methods, the SLCABG model combines the advantages of sentiment lexicon and deep learning technology, and overcomes the shortcomings of existing sentiment analysis model of product reviews. The SLCABG model combines the advantages of the sentiment lexicon and deep learning techniques. First, the sentiment lexicon is used to enhance the sentiment features in the reviews. Then the CNN and the Gated Recurrent Unit (GRU) network are used to extract the main sentiment features and context features in the reviews and use the attention mechanism to weight. And finally classify the weighted sentiment features. In terms of data, this paper crawls and cleans the real book evaluation of dangdang.com, a famous Chinese e-commerce website, for training and testing, all of which are based on Chinese. The scale of the data has reached 100000 orders of magnitude, which can be widely used in the field of Chinese sentiment analysis. The experimental results show that the model can effectively improve the performance of text sentiment analysis.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/88805
Identification Number/DOI	10.1109/ACCESS.2020.2969854
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Biomedical Sciences Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Publisher	IEEE
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