Journal of Guangxi Normal University(Natural Science Edition) ›› 2019, Vol. 37 ›› Issue (1): 50-61.doi: 10.16088/j.issn.1001-6600.2019.01.006

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Analysis of Text Emotion Cause Based on Multi-task Deep Learning

YU Chuanming1,LI Haonan2,AN Lu3*   

  1. 1. School of Information and Security Engineering, Zhongnan University of Economics and Law, Wuhan Hubei 430073, China;
    2. School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan Hubei 430073, China;
    3. School of Information Management, Wuhan University, Wuhan Hubei 430072, China
  • Received:2018-10-14 Online:2019-01-20 Published:2019-01-08

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Abstract: Multi-task learning utilizes the similarity between different tasks to help decision making. Compared with single-task learning, multi-task learning can use more information, which can make up for the deficiency of single-task learning in the use of information. In this paper, a NTCIR-ECA dataset, which contains Chinese and English text data is used as the date in the experiment. The emotional cause analysis is regarded as the research task and a multi-task deep learning model (MTDLM) which combines multi-task learning and deep learning is presented. Finally, this model is used to do the emotional cause analysis in different languages. The experimental results show that in the case of unbalanced data, the optimal F value of the MTDLM model for English language emotion recognition is 39%, superior to single task learning (F value is 0) and traditional baseline model (LR, F value is 33%). The validity of the model is thus verified.

Key words: emotion cause analysis, multi-task learning, deep learning, text-mining

CLC Number: 

  • TP391
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