黑龙江科技大学学报2017，Vol.27Issue(6)：698-704,7.DOI:10.3969/j.issn.2095-7262.2017.06.025
在线评论的产品缺陷识别方法
Product defect recognition method for online reviews
刘丹 1张玉红 1闫相斌1
作者信息
- 1. 哈尔滨工业大学 经济与管理学院,哈尔滨150001
- 折叠
摘要
Abstract
This paper describes an effort to analyze the defects of enterprise products from Internet users comments. The analysis involves performing text classification of the product reviews using semi su-pervised classification algorithm based on user Co-forest differences; providing defect topic clustering of defect review identified by Co-forest algorithm based on BTM algorithm based on topic model in a way that affords the defect theme, topic description details, and the proportion; and conducting related research on the Jingdong website comments using a brand of a best-selling dehumidifier as an example. The results show that the Co-forest algorithm boasts a higher performance than supervised classification and semi su-pervised classification Tri-training method in terms of defect recognition and classification based on online reviews.关键词
缺陷识别/在线评论/半监督分类/主题聚类/除湿机Key words
defect identification/online reviews/semi-supervised classification/topic clustering/de-humidifier分类
信息技术与安全科学引用本文复制引用
刘丹,张玉红,闫相斌..在线评论的产品缺陷识别方法[J].黑龙江科技大学学报,2017,27(6):698-704,7.
