Research on multimodal clustering method for E-commerce review

• Main Authors: Zhu, Feiyu, Wang, Zhuo
• Format: Conference Proceeding
• Language: English
• Published: SPIE 25.05.2023
• ISBN: 1510664815; 9781510664814
• ISSN: 0277-786X
• DOI: 10.1117/12.2675228

In an increasingly information-based modern society, online shopping has gradually become the first choice for people to buy goods. Therefore, more and more e-commerce platforms have emerged, such as Tmall, JD, etc., and e-commerce economy has gradually become an important part of promoting social progress. Since most e-commerce reviews are short texts, it takes a lot of time for consumers to search for useful information for themselves. Therefore, this paper clusters short texts for the first time, and takes the user's review text as the input of the model to get the clustering results. The review text with similar views will be grouped into the same cluster, At the same time, we can get the topic words in each cluster, that is, where consumers pay most attention in the consumption process. Due to the large number of comment texts, we use Dirichlet and multinomial mixed model as the first clustering model, because this model does not need to set the clusters number in advance, and has good performance on large-scale data sets. For the results of the first clustering, although consumers' concerns can be analysed, it is difficult to judge consumers' attitudes, so we take the subject words in the clustering results as input to form a new short text, use TF-IDF to extract the features of the short text constructed by the subject words, then use K-means method to judge the emotional polarity, and then use homogeneity, integrity NMI effectively evaluates the clustering results. Through the experimental results of two clusters, we can clearly find out where consumers pay most attention after purchasing a certain commodity. For example, after purchasing clothes, most consumers focus on the materials and brands of clothes. After clustering, we can get the clustering results of all consumers and the clustering subject words, that is, consumers in the same cluster have similar concerns about goods. After the second clustering, the emotional polarity of consumers can be analyzed, which is of positive significance to consumers' purchase decisions.