Extraction of Product Defects and Opinions from Customer Reviews by Using Text Clustering and Sentiment Analysis

被引：2

作者：

Cataltas, Mustafa ^{[1
]}

Dogramaci, Sevcan ^{[1
]}

Yumusak, Semih ^{[2
]}

Oztoprak, Kasim ^{[1
]}

机构：

[1] Konya Food & Agr Univ, Comp Engn, Konya, Turkey

[2] KTO Karatay Univ, Comp Engn, Konya, Turkey

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2020年

关键词：

natural language processing; feature extraction; opinion mining; text summarization;

D O I：

10.1109/BigData50022.2020.9377851

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The development of e-commerce has created new shopping trends of customers. In online shopping environments, product reviews play a critical role in the choice of customers. Online reviews are additionally valuable for the manufacturers and the vendors by providing easily accessible feedback to them. In this study, a text analysis method is proposed to find the defective features of the products by detecting features with negative opinion tendency in the clustered customer reviews. The output of the proposed model, the extracted defects, may provide a strong source of guidance both for consumers in purchase decisions and for producers in product improvement.

引用

页码：4529 / 4534

页数：6

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