TY - GEN
T1 - A categorized sentiment analysis of Chinese reviews by mining dependency in product features and opinions from blogs
AU - Kao, Hung-Yu
AU - Lin, Zi Yu
PY - 2010/12/13
Y1 - 2010/12/13
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78649885983&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78649885983&partnerID=8YFLogxK
U2 - 10.1109/WI-IAT.2010.298
DO - 10.1109/WI-IAT.2010.298
M3 - Conference contribution
AN - SCOPUS:78649885983
SN - 9780769541914
T3 - Proceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
SP - 456
EP - 459
BT - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
T2 - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
Y2 - 31 August 2010 through 3 September 2010
ER -