Abstract
A three-way decision framework based on fuzzy concepts can better understand uncertain decision boundaries by automatically dividing opinions into three decision regions: positive, negative, and boundary regions. However, when the fuzzy values of the opinions are very close, the boundary regions tend to be large. The difficult problem is how to significantly reduce the boundary regions while maintaining high classification accuracy. The most useful opinion features include fuzzy concepts and semantic features (or patterns). Unlike fuzzy concepts, semantic features are represented by using semantic patterns that frequently appear in opinions. We also observe a low statistical correlation between semantic patterns and fuzzy concepts. Therefore, this paper proposes a novel opinion classification method that integrates semantic patterns with fuzzy concepts in a three-way decision framework. The new method can increase the discriminative power of classifying opinions using features. Experimental results verify that the integration of fuzzy concepts and semantic patterns has better classification performance than using fuzzy concepts alone. This also shows that fusing multiple features is an effective solution to represent opinions more meaningfully, thereby effectively improving classification accuracy and reducing uncertain boundaries in three-way opinion classification.
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