Abstract To solve the problems of cost imbalance in text sentiment analysis and high classification cost in static decision-making, a cost-sensitive text sentiment analysis method is constructed based on sequential three-way decision, and the misclassification cost and learning cost in dynamic decision-making process are taken into account. Firstly, a granulation model for text data is proposed to construct a multi-level granular structure. Next, sequential three-way decision is introduced to set a dynamic text analysis framework. Finally, real text review datasets are utilized to validate the effectiveness of the proposed method. Experimental results show that the proposed method significantly reduces the overall decision-making cost with the improved classification quality.
Fund:Supported by National Natural Science Foundation of China(No.61876157, 71571148), Yanghua Scholar Plan(Part A) of SWJTU(No.201806)
Corresponding Authors:
LIU Dun, Ph.D., professor. His research interests include data mining, knowledge discovery, rough set, granular computing and decision support systems.
About author:: FAN Qin, master student. Her research interests include data mining, knowledge discovery, three-way decision and granular computing.
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