Title :
A Categorized Sentiment Analysis of Chinese Reviews by Mining Dependency in Product Features and Opinions from Blogs
Author :
Kao, Hung-Yu ; Lin, Zi-Yu
Author_Institution :
Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
fDate :
Aug. 31 2010-Sept. 3 2010
Abstract :
In the past, there have been many documents focusing on English reviews for sentiment analysis. These contain abundant research results which extract features and opinions, identify semantic orientation, and associate features with opinions. Although this approach has performed well for English reviews, it is not as successful with Chinese reviews. In this paper, we aim to develop a sentiment analysis system that is suitable for Chinese reviews. This system would extract features that users are interested in and detect those opinions with semantic orientations that accord with the dependency of certain features and opinions in one specific category. We then present users with the integrated results. Our experiments show that the derived system can effectively measure the dependency between features and opinions. The prominent performance of review sentiment analysis also validates the applicability of the proposed method.
Keywords :
Web sites; data mining; reviews; Chinese reviews; blogs; categorized sentiment analysis; dependency mining; Blog; Opinion Mining; Sentiment Analysis;
Conference_Titel :
Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on
Conference_Location :
Toronto, ON
Print_ISBN :
978-1-4244-8482-9
Electronic_ISBN :
978-0-7695-4191-4
DOI :
10.1109/WI-IAT.2010.298