Computer Science ›› 2019, Vol. 46 ›› Issue (8): 50-55.doi: 10.11896/j.issn.1002-137X.2019.08.008

• Big Data & Data Science • Previous Articles     Next Articles

User Reviews Clustering Method Based on Topic Analysis

ZHANG Hui-bing1, ZHONG Hao1, HU Xiao-li2   

  1. (Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China)1
    (Practice and Experiment Station,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China)2
  • Received:2018-07-19 Online:2019-08-15 Published:2019-08-15

Abstract: The rational clustering analysis of user reviews in social business is beneficial to providing accurate service or recommendation information.This paper proposed a user reviews clustering method based on topic analysis.According to the mutual information intensity of topic words in user reviews and the similarity between topic words,the weight of topic words is calculated,and the topic vector of user reviews is constructed.On this basis,an adaptive canopy+kmeans clustering algorithm based on user comment similarity to automatically select the initial threshold of canopy clustering algorithm is proposed,which is used to cluster and analyze the subject vector.On Amazon’s review data,the results show that the proposed method makes full use of the weight of different topic words in the user’s reviews and improves the disadvantage of the K-means clustering algorithm easily falling into the local optimal.Compared with the traditional LDA+K-means algorithm,the proposed method can achieve better results

Key words: User reviews, Topic analysis, Topic vector, Adaptive clustering

CLC Number: 

  • TP399
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