摘要：

Abstract:

【Purpose/significance】Online reviews, especially negative reviews, are an important basis for consumers to make purchas⁃

ing decisions. However, the existing methods for reducing information redundancy still need to be improved in negative online reviews.

【Method/process】This paper proposes a clustering method of negative review spectrum based on Gaussian LDA. First, the Gaussian

LDA model is used to obtain the topic distribution in negative reviews, then the Pearson similarity between reviews is calculated based

on the topic distribution, and the negative clustering is implemented using a spectral clustering algorithm. Finally, each class is near⁃

est to the cluster center M comments are representative of this category.【Result/conclusion】Gaussian LDA, LDA, TF-IDF and

Doc2Vec were combined with spectral clustering respectively, and cross-comparison of Gaussian LDA with K-means, DBSCAN, and

spectral clustering was performed to verify the superiority of the method Sex. The negative comments extracted based on this are high⁃

ly differentiated and highly representative, which solves the problem of information redundancy.【Innovation/limitation】The

multi-model fusion clustering method that firstly extracting topics and then clustering provides a new method and ideas for solving the

problem of review information redundancy. It also provides a new reference for the study of text mining and text clustering.