该文利用领域本体对产品评论文本中的评价对象进行抽取和整合,在此基础上,建立产品性能的非完备信息系统,将特征的情感倾向寓于特征的权重计算之中。对非完备信息系统,给出了基于差别矩阵的启发式特征约简方法,通过特征降维处理,达到了减少特征的冗余度和数据稀疏性的目的。对降维后的非完备信息系统采用K-Means聚类算法,实现了评价对象情感聚类。为了验证该文提出方法的有效性,在真实汽车评论文本数据上进行实验, 实验结果表明,在对特征进行一定程度的降维后,仍表现出较好的聚类效果。

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