| With the advent of the Internet era,many Internet companies have caught up with the express train of the era and ushered in high-speed development.People have browsed information on the Internet and generated a lot of data.Consumers’ comments on the products left on the platform can not only provide purchase decisions for other consumers,but also provide directions for manufacturers’ development.Therefore,we conduct sentiment analysis on the review text data left by consumers on the e-commerce platform,explore the value contained in the review data,and adjust strategies to improve the research and development direction,so that the competitiveness of manufacturers and platforms is improved.Based on this,this article first selects the bracelet products on the domestic e-commerce platform for research,obtains user reviews as a research data set,and after preprocessing it,uses a simple Bayesian classifier for training and testing,and predicts The sentiment value of the review data.In the analysis of the topic model,this paper uses the TF-IDF value to improve the extraction and analysis of the LDA topic model,uses ROSTCM6 to visualize the semantic network.On this basis,further research on the product recommendation algorithm model is introduced,the attention mechanism is introduced to improve the traditional LSTM model and the two-way deep neural network is constructed,the improved LSTM and STV model are combined to construct the SVD-DS product recommendation model,On Amazon’s public test set,the current popular recommendation system model Predict Mean,the basic SVD and the most recent SVD + KIMCNN model are compared and verified.After comparing the test indicators of multiple models,it is shown that the SVD-DS model is in the product review The data set has better performance than other models. |
