Recommended or Not Recommended? Review Classification through Opinion Extraction

Author

Feng, Sheng ; Zhang, Ming ; Zhang, Yanxing ; Deng, Zhihong

Author_Institution

Sch. of Electron. Eng. & Comput. Sci., Peking Univ., Beijing, China

fYear

2010

fDate

6-8 April 2010

Firstpage

350

Lastpage

352

Abstract

With the rapid growth of web 2.0, online product reviews generated by users are becoming increasingly useful for customers to make purchase decisions. In this paper, we focus on the problem of classifying user reviews as recommended the product or not. The proposed method first mines the product features and relevant opinions, and then determines the overall sentiment orientation of the review based on the polarity and strength of these opinions. The evaluation results show the effectiveness of our proposed method in product feature mining and review classification.

Keywords

data mining; pattern classification; recommender systems; user interfaces; Web 2.0; online product reviews; opinion extraction; product feature mining; product opinion mining; review classification; sentiment orientation; user reviews; Batteries; Computer science; Data mining; Feature extraction; Machine learning; Motion pictures; Speech; Tagging; Training data; Writing; opinion mining; sentiment analysis; user review;

fLanguage

English

Publisher

ieee

Conference_Titel

Web Conference (APWEB), 2010 12th International Asia-Pacific

Conference_Location

Busan

Print_ISBN

978-1-7695-4012-2

Electronic_ISBN

978-1-4244-6600-9

Type

conf

DOI

10.1109/APWeb.2010.38

Filename

5474114