Computer Science ›› 2021, Vol. 48 ›› Issue (11): 312-318.doi: 10.11896/jsjkx.200900088

• Artificial Intelligence • Previous Articles     Next Articles

Construction and Application of Russian Multimodal Emotion Corpus

XU Lin-hong1, LIU Xin1, YUAN Wei2, QI Rui-hua1   

  1. 1 Research Center for Language Intelligence of Dalian University of Foreign Languages,Dalian,Liaoning 116044,China
    2 Information Engineering University,Luoyang,Henan 471003,China
  • Received:2020-09-10 Revised:2021-03-26 Online:2021-11-15 Published:2021-11-10
  • About author:XU Lin-hong,born in 1979,associate professor.Her main research interests include nature language processing and sentiment analysis.
  • Supported by:
    Ministry of Education Humanities and Social Science Project(18YJCZH208) and National Natural Science Foundation of China(61806038,61772103).

Abstract: As a research hotspot in the field of emotion analysis,Russian multimodal sentiment analysis technology can automatically analyze and identify emotions through rich information such as text,voice and image,which is helpful to timely understand the public opinion hotspots in Russian speaking countries and areas.However,there are only a few multimodal emotion corpora in Russian,which limits the further development of Russian emotion analysis technology.Based on the analysis of the related research and emotion classification methods of multimodal emotion corpus,this paper develops a scientific and complete tagging system,which includes 11 items of information in utterance,space-time and emotion.In the whole process of corpus construction and quality control,this paper follows the principle of emotional subject and emotional continuity,formulates a strong operational annotation specification and constructs a large-scale Russian emotional corpus.Finally,it discusses the application of corpus in the analysis of emotional expression characteristics,the analysis of personality characteristics and the construction of emotion recognition model.

Key words: Corpus, Multimodal, Russian, Sentiment analysis

CLC Number: 

  • TP393
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