The popularity of the Internet has brought profound influence to electronic commerce. A kind of review-oriented consumption mode is gradually expanding in the market and consumers will refer to the reviews provided by consumers who bought the product in the past. How to accurately analyze users' sentiments from massive data of e-commerce reviews has become one of the key issues for e-commerce platforms. Current standard sentiment analysis classifies overall sentiment of e-commerce reviews without an extended description of the entity. We set up an optimized Aspect-based sentiment analysis (ABSA) that includes four elements: aspect, category, polarity, and opinion. Aiming at the above problems, this paper proposes a Chinese e-commerce reviews sentiment analysis algorithm based on BERT. By using pre-training model, we use the BIO(B-begin,I-inside,O-outside) data labeling pattern to label entities and study sentiment analysis by the annotation data. Experimental results on the Taobao cosmetics review datasets show that compared with the ordinary deep learning methods, our approach in the accuracy rate and the F1 score has significant improvement.
|Title of host publication||Proceedings - 2020 IEEE 18th International Conference on Industrial Informatics, INDIN 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 7 Jun 2021|
|Event||18th IEEE International Conference on Industrial Informatics - Warwick, Virtual, Warwick, United Kingdom|
Duration: 21 Jul 2020 → 23 Jul 2020
|Name||IEEE International Conference on Industrial Informatics (INDIN)|
|Conference||18th IEEE International Conference on Industrial Informatics|
|Abbreviated title||INDIN 2020|
|Period||21/07/20 → 23/07/20|
Bibliographical notePublisher Copyright:
© 2020 IEEE.
- e-commerce reviews
- sentiment analysis