###### Abstract  
The development of e-commerce has ushered in a golden age. E-commerce product reviews are remarks initiated by online shopping users to evaluate the quality and service of the products they purchase; these reviews help consumers learn the reality of the product. The sentiment polarity of e-commerce product reviews is the best way to obtain customer feedback on products. Therefore, sentiment analysis of product reviews on e-commerce platforms is greatly significant. However, the challenges of sentiment analysis of Chinese e-commerce product reviews lie in dimension mapping, disambiguation of sentiment words, and polysemy of Chinese words. To solve the above problems, this paper proposes a sentiment analysis model ERNIE-BiLSTM-Att (EBLA). Here, the dynamic word vector generated using the Enhanced Representation through Knowledge Integration (ERNIE) word embedding model is input into the Bidirectional Long Short-term Memory (BiLSTM) to extract text features. Then, the Attention Mechanism (Att) is used to optimize the weight of the hidden layer. Finally, _softmax_ is used as the output layer for sentiment classification. The experimental results on the JD.com Chinese e-commerce product review dataset show that the proposed model achieves more than 0.87 in precision, recall, and \(F1\) values, which is superior to classic deep learning models proposed by other researchers; it has strong practicability in sentiment analysis of Chinese e-commerce product reviews. ERNIE; attention mechanism; BiLSTM; sentiment analysis; deep learning; Chinese e-commerce product reviews +
Footnote †: journal: Applied Sciences  
e-commerce platforms. These comments and opinions express the sentiment tendency of consumers who have purchased products, and the commodity-related comments provided by them greatly influence the purchase intentions of other consumers [4]. Consumers are accustomed to consulting product reviews before purchase to reduce perceived uncertainty and online shopping risks due to information asymmetry. The sentiment polarity of e-commerce product reviews is the best way to obtain customer feedback on products. Therefore, positive reviews establish a good brand image for businesses to attract more consumers. Neutral reviews make businesses realize the insufficiency in some aspects of products or services. However, negative reviews greatly affect consumers' purchase intention, product sales, and the normal operation of enterprises. Therefore, sentiment analysis of product reviews on e-commerce platforms is greatly significant. Sentiment analysis, also known as opinion mining [5], was originally proposed by [PERSON] [6]. It is the analysis of a certain product's review to find consumers' attitudes and opinions about the product. [PERSON] [7] further elaborated the concept of sentiment analysis research: sentiment analysis research or opinion mining refers to the statistical analysis of people's views, emotions, judgments, and attitudes toward commodities, businesses, groups, people, social problems, social events, and their basic attributes. More so, sentiment analysis of e-commerce product reviews is a process of analyzing the content of user reviews on e-commerce platforms and mining the attitudes of these reviews. Recently, sentiment analysis research on e-commerce product reviews has become a hot topic. [PERSON] et al. [8] used word2 vec to learn word embedding and convolution neural networks to train and classify the sentiment classes of the product reviews. [PERSON] et al. [9] proposed a parallel Convolutional Neural Networks (CNN) and BiLSTM-attention two-channel neural network for sentiment analysis of e-commerce product reviews. However, although the above models have achieved good results, they have not considered dimension mapping, disambiguation of sentiment words, and polysemy of words in input. The sentiment analysis of e-commerce product reviews is considered a multidimensional classification process, which includes three dimensions: document, sentence, and aspect level tasks.