Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (3): 218-224.doi: 10.11896/jsjkx.210400034

• Artificial Intelligence • Previous Articles     Next Articles

Transferable Emotion Analysis Method for Cross-domain Text

ZHANG Shu-meng1, YU Zeng1, LI Tian-rui1,2   

  1. 1 School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China
    2 National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Chengdu 611756,China
  • Received:2021-04-02 Revised:2021-07-28 Online:2022-03-15 Published:2022-03-15
  • About author:ZHANG Shu-meng,born in 1996,postgraduate.Her main research interests include natural language processing and sentiment analysis.
    LI Tian-rui,born in 1969,Ph.D,professor,Ph.D supervisor,is a distinguished member of China Computer Federation.His main research interests include big data intelligence,rough sets and granular computing.
  • Supported by:
    National Key R & D Program of China(2020AAA0105101).

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Abstract: With the rapid development of mobile internet,social network platform is full of a large number of text data with emotional color.Mining the emotion in such text not only helps to understand the attitude and emotion of internet users,but also plays an important role in scientific research institutions and the government to grasp the emotional changes and trends of society.Traditional sentiment analysis mainly focuses on the analysis of sentiment tendency,which can not accurately and multi-dimensionally describe the emotion of the text.In order to solve this problem,this paper studies the emotion analysis of the text.Firstly,aiming at the lack of fine-grained sentiment tags in text data sets of different fields,a deep learning based emotion analysis model,FMRo-BLA,is proposed.The model pre-trains the general domain text,and then applies the pre-trained model to the downstream situation of specific domain through parameter based migration learning,feature fusion and FGM Adversarial training.Compared with the best performance of RoBERTa pre-trained language model,the F1 value of the proposed method is improved by 5.93% on the target domain dataset,and it achieves 12.38% by further adding transfer learning.

Key words: Deep learning, Emotion analysis, Feature fusion, Transfer learning

CLC Number: 

  • TP391
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