Research on Recommendation Algorithm Based on Cross-grained Emotion Analysis

In the era of internet and big data, traditional method of user preferences mining has been difficult to keep up with the update speed of enterprise product or service decision adjustment, so it is a new idea to apply recommendation algorithm to user preferences mining. Most of the recommendation algorithms based on review emotion analysis are carried out at a single level of fine-granularity or coarse-granularity, which is difficult to ensure the accuracy and comprehensiveness of user preferences mining. This paper proposes a new recommendation algorithm EAFM, which is based on cross-grained emotion analysis. Based on the latent dirichlet allocation, dependency syntactic analysis and convolutional neural network model, the algorithm synchronously performs fine-grained and coarse-grained emotion analysis with online review data as corpus, and then proposes the emotion score correction mechanism, which solves the problems of data sparsity and algorithm time complexity in user preference mining. In the experimental design section, we use Amazon product data for verification, and regard root mean square error as the performance evaluation index. Experimental results show that the EAFM approach has better user preference mining performance than the compared algorithm.