Fine-grained Aspect Extraction for Online Reviews of E-commerce Products Based on Semi-supervised Learning

摘要：

　　The accuracy of online review mining for e-commerce products is of great value to customer and product matching portrait.Mining the fine-grained aspect in reviews is a key indicator.It can better analyze the emotion tendency of online reviews and understand the advantages and disadvantages of evaluation objects.In this paper, we propose a semi-supervised learning method to extract product aspects and description of aspects.Specifically, we firstly construct word vector space model of large scale reviews with deep learning, then get the list of similar words based on the model.Finally,the fine-grained aspect sets are obtained by classification algorithm.The results of the study show that the efficiency of fine-grained extraction is improved by using semi-supervised method.

关键词： Fine-grained aspect car reviews aspect extraction deep learning

作者: Huosong Xia Yitai Yang

作者单位: School of Management, Wuhan Textile University, Wuhan, 430073, China

会议类型: 国际会议

会议名称: The Seventeenth Wuhan International Conference on E-Business(第17届武汉电子商务国际会议)

会议地点: 武汉

会议语种:英文

页码: 165-172

在线出版日期: 2018-05-25（万方平台首次上网日期，不代表论文的发表时间）