## 5 Conclusions  
The rapid development of computer and network technology has seen a rapid increase in the number of Internet users and has become a platform for e-commerce to flourish. There are many products on the e-commerce platform, and the quality is uneven, which is prone to the phenomenon of inconsistency between product promotion and reality. To a certain extent, the massive comments on e-commerce platforms reflect the real information about the products, and the sentiment polarity in these comment texts contains the real feedback from consumers on the products. Therefore, the sentiment analysis of these e-commerce product reviews is of great significance.  
Deep learning is currently the most popular technique in sentiment analysis, but the classification performance of many existing traditional models can be further improved. In this paper, we designed the EBLA sentiment analysis model to perform sentiment analysis on the Chinese e-commerce product reviews. The model obtains a more accurate dynamic text semantic representation using the ERNIE word embedding model, extracts contextual features using a bidirectional LSTM, combines the attention mechanism to highlight the features highly relevant to the training task, and performs sentiment classification. The problem of dimension mapping, sentiment word disambiguation, and word polysemy in the sentiment analysis of Chinese e-commerce product reviews is well handled. The experimental results on the JD.com (accessed on 2 March 2022) e-commerce product review dataset show that the model achieved a better classification effect in the sentiment analysis task of Chinese e-commerce product reviews.  
Based on our research, we found two directions for our future work. One direction is that the ERNIE pre-training language model has high hardware requirements and needs a massive data corpus as support. How to train a more lightweight ERNIE model is one of our future works. Another direction is that for some customers and business, it is not enough to know only the overall sentiment polarity of reviews; they would like to realize what aspects of the product or service are negative, neutral, or positive from other consumer reviews. Therefore, our future research can be extended to aspect-level sentiment analysis of e-commerce product reviews, and more detailed mining of consumers' specific opinions on various attributes of products, so that other consumers can further choose their favorite products according to the product attributes they value more, but also enables merchants to improve a specific aspect of the product.  
**GitHub:**[[https://github.com/[PERSON]/Sentiment-analysis-model-ERNIE-BiLSTM-Att](https://github.com/[PERSON]/Sentiment-analysis-model-ERNIE-BiLSTM-Att)]([https://github.com/[PERSON]/Sentiment-analysis-model-ERNIE-BiLSTM-Att](https://github.com/[PERSON]/Sentiment-analysis-model-ERNIE-BiLSTM-Att)).  
**Author Contributions:** Conceptualization, W.H. and M.L.; methodology, W.H; software, M.L. and Y.W.; validation, M.L.; formal analysis, W.H. and M.L. data curation, M.L.; writing--original draft preparation, M.L.; writing--review and editing, W.H.; supervision, Y.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Major Projects of National Social Science Fund of China (No. 16 ZDA054) and the Jiangsu Provincial Department of education, the key research base of philosophy and social sciences.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.