C2M E-commerce Service Quality Evaluation Based on LDA-BiLSTM Model

In the context of digital economy, the C2M model, as a new e-commerce model to meet customers’ personalized needs, provides more efficient, convenient and personalized services. In order to explore the evaluation of C2M e-commerce service quality, a C2M e-commerce service quality evaluation model based on LDA theme model and BiLSTM model is proposed. Deep learning has powerful feature selection and feature extraction capabilities, which can automatically capture more abstract and comprehensive semantic information of text, combine topic mining with emotion analysis, and help to identify the needs of users. The LDA theme model is used to extract 8 C2M e-commerce service quality themes; analyzing the service quality themes extracted by LDA based on BiLSTM model, the accuracy, recall rate and F1 factor of Bi-LSTM model are above 90%, indicating that it can accurately predict the emotional tendency of the text, and further select the themes with more negative comments. According to the emotion analysis results, consumers have high negative emotional tendency of commodity cost performance, quality control, personalized customization and logistics service, which are 0.28,0.26,0.25 and 0.21 respectively. Analyanalyze the causes of negative emotions, and put forward targeted suggestions to improve the service quality of C2M e-commerce. The method of this paper makes the C2M e-commerce comment text more expressive, which helps the C2M e-commerce company to better understand the needs of customers and improve the service quality.