Sentiment Clustering of Evaluation Object Based on Incomplete Information Systems
Yunyun Lv · Zhongwen xinxi xuebao · 2012
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.