Product evaluation analysis model based on combined action of multiple factors

With the rapid development of e-commerce, the product review data is also rapidly expanding, which make it possible to obtain user attitudes from the review data and analyze the performance of product attributes. Effectively mining this information for design services is undoubtedly of great significance to product innovation. However, the existing product evaluation model lacks the consideration of the correlation between evaluation indicators, which easily leads to deviation of evaluation results. For this reason, a new method of improved interaction matrix was proposed. According to the frequency of attribute words mentioned in the review data, the user's value coefficient was determined for each attribute, and FPGrowth algorithm was used to mine the relationship between attributes. These two factors were used as the main diagonal and non-main diagonal of the matrix to form an improvement matrix, and a product evaluation analysis model was constructed based on this matrix. The relevance influence between the user's viewpoint and the evaluation elements was fully considered, and the deviation of the existing evaluation model structure was corrected. The comprehensive score of the product was calculated by analyzing the user's emotional attitude contained in the comment data. Taking the electric toothbrush on the B2C Website as an example, the feasibility and effectiveness of the proposed evaluation model was verified. © 2022 CIMS. All rights reserved.