Research on e-business image-text review mining based on depth feature fusion

Abstract

The comment data of e-commerce platform is an important basis for consumers and marketers to make decisions. With the development of platform related technologies, the scale of image comment data shows an increasing trend with stronger information expression ability. At present, the research on the related technology of comment image and comment text is relatively mature, and how to effectively integrate the image part and text part is the focus of image-text comment data research. This paper proposes an e-commerce image-text comment mining model based on depth feature fusion. According to different stages, the model is mainly divided into four stages: image-text comment data acquisition, pre-processing, feature extraction and feature fusion. The e-commerce image-text comment data set is constructed through the data acquisition and pre-processing. In the feature extraction and fusion stage, the graphic information fusion is realized by designing the structure of deep neural network model. The fused features are applied to downstream mining tasks. Finally, the experiment on the data set shows that the classification accuracy of the model is higher than that of single text or single image, which shows that the model can effectively improve the mining effect of image-text comments.