Three-Way Framework Using Fuzzy Concepts and Semantic Rules in Opinion Classification

Abstract

Binary classification is a critical process for opinion mining, which classifies opinions or user reviews into positive or negative classes. So far many popular binary classifiers have been used in opinion mining. The problematic issue is that there is a significant uncertain boundary between positive and negative classes as user reviews (or opinions) include many uncertainties. Many researchers have developed models to solve this uncertainty problem. However, the problem of broad uncertain boundaries still remains with these models. This paper proposes a threeway decision framework using semantic rules and fuzzy concepts together to solve the problem of uncertainty in opinion mining. This framework uses semantic rules in fuzzy concepts to enhance the existing three-way decision framework proposed by authors. The experimental results show that the proposed three-way framework effectively deals with uncertainties in opinions using relevant semantic rules.