Title :
Recommended or Not? Give Advice on Online Products
Author :
Qin, Bing ; Zhao, Yanyan ; Gao, Leilei ; Liu, Ting
Author_Institution :
Inf. Retrieval Lab., Harbin Inst. of Technol., Harbin
Abstract :
This paper introduces an opinion judgment system that automatically gives advice on whether to recommend this product and furthermore provides corresponding reasons.The core task of the system can be considered as a binary sentence sentiment classification problem. A novel "polarityword-target" related feature extraction method is proposed. An opinion judgment system is then built based on the sentiment of each sentence predicted by a maximum entropy (ME) classifier with the novel features. The experimental results on two domains show that the special feature extraction methods are promising; the opinion judgment system with 91.5% average accuracy is highly effective.
Keywords :
Internet; classification; information filters; information retrieval; binary sentence sentiment classification problem; feature extraction; maximum entropy classifier; online products; opinion judgment system; polarityword-target; product recommendation; Batteries; Colored noise; Data mining; Digital cameras; Entropy; Feature extraction; Fuzzy systems; Information retrieval; Laboratories; Noise level;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on
Conference_Location :
Jinan Shandong
Print_ISBN :
978-0-7695-3305-6
DOI :
10.1109/FSKD.2008.441