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| Sequential Three-Way Sentiment Analysis Based on Temporal-Spatial Multi-granularity |
| YANG Xin1, LIU Dun2, LI Qiuke1, YANG Xibei3 |
1. School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130
2. School of Economics and Management, Southwest Jiaotong University, Chengdu 610031
3. School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212003 |
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Abstract The traditional static methods of sentiment analysis cannot meet the quantity and complexity requirements of dynamic data in the big data era. Therefore, grounded on the concept of sequential three-way decisions, a sequential three-way sentiment analysis framework based on temporal-spatial multi-granularity is proposed to overcome the shortcomings of the traditional two-way decisions. Firstly, a multi-layer granular structure with temporal-spatial features is constructed using increasing data and better-fitting feature space over time to balance the misclassification cost and training cost. Then, three typical sentiment classification methods are applied as benchmarks to test the efficiency of the proposed method. Finally, compared with the static methods, experimental results on two datasets show that the proposed method greatly reduces the classification costs with the classification quality maintained.
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Received: 15 June 2020
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| Fund:Supported by National Natural Science Foundation of China(No.61876157), Humanity and Social Science Youth Foundation of Ministry of Education of China(No.20YJC630191), Fundamental Research Funds for the Central Universities(No.JBK2001004) |
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Corresponding Authors:
YANG Xin, Ph.D., associate professor. His research interests include data mining and three-way multi-granularity learning.
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| About author:: YANG Xibei, Ph.D., professor. His research interests include machine learning, rough sets and granular computing. LIU Dun,Ph.D.,professor.His research interests include data mining,knowledge discovery,rough sets and granular computing. LI Qiuke,undergraduate student. |
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2020, Vol. 33 
