Sentiment Clustering of Evaluation Object Based on Incomplete Information Systems
WANG Suge1,2, YIN Xueqian3, LI Ru1,2, ZHANG Jie3, LV Yunyun1
1. School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China (2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, Shanxi 030006, China (3. School of Mathematics Science, Shanxi University, Taiyuan, Shanxi 030006, China
Abstract：Based on the evaluation objects extraction form product review texts via the domain ontology, an incomplete information system for the product performance is established, which deals with the feature sentiment orientation by the feature weighting. A heuristic feature dimension reduction method is proposed based on discernibility matrix to reduce redundancy and data sparsity. K-Means clustering algorithm is utilized for realizing evaluation objects clustering. On the car review corpus, the proposed method produces the best performance after feature dimension reduction in a certainty extent in terms of the sentiment clustering of the evaluation objects. Key wordsincomplete information systems; evaluation object; ontology; feature dimension reduction; clustering
2012, Vol. 26 
