Discovering e-commerce user groups from online comments: An emotional correlation analysis-based clustering method

Platform merchants mine user clusters and their characteristics to assist in precision marketing. In view of the information overload in e-commerce reviews, machine methods are needed to efficiently obtain clustering information from text. This study innovatively integrated the emotional correlation analysis model and Self-organizing Map (SOM) in application, to construct finegrained user emotion vector based on review text and perform visual cluster analysis, which helped quickly mine user clustering and characteristics from review text. The result of empirical analysis based on real reviews of Amazon books showed that the proposed method had the average precision as 0.71, confirming that the clustering method integrating the emotional correlation analysis model and SOM could efficiently mine user groups and match appropriate marketing strategies, which will help platform merchants carry out precision marketing. The study makes contributions to the application and innovation of researches in the field of user clustering and e-commerce precision marketing.

Automated summary

This study integrates the emotional correlation analysis model and Self-organizing Map (SOM) to construct fine-grained user emotion vector based on review text and perform visual cluster analysis, which helps platform merchants quickly mine user clustering and characteristics.