ew 15(4): e2

Research Article

Research on Clothing Product Reviews Mining Based on the Maximum Entropy

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  • @ARTICLE{10.4108/eai.19-8-2015.2260919,
        author={Pengfei Feng and Qinghong Yang},
        title={Research on Clothing Product Reviews Mining Based on the Maximum Entropy},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={2},
        number={4},
        publisher={EAI},
        journal_a={EW},
        year={2015},
        month={8},
        keywords={association rules; the maximum entropy; review classification},
        doi={10.4108/eai.19-8-2015.2260919}
    }
    
  • Pengfei Feng
    Qinghong Yang
    Year: 2015
    Research on Clothing Product Reviews Mining Based on the Maximum Entropy
    EW
    EAI
    DOI: 10.4108/eai.19-8-2015.2260919
Pengfei Feng1,*, Qinghong Yang1
  • 1: Beihang University
*Contact email: fengbuaap@163.com

Abstract

this paper excavated the review theme of clothing products by method of association rules, and built a maximum entropy model for the reviews classification. Then this paper did experimental verification to large-scale clothing product reviews classification, which verified the practical effect that maximum entropy model had in the comment text classification problems. In the process of classification, the maximum entropy model had a good effect, of which accuracy was over 90%.