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Emotion Detection in Cross-Lingual Text Based on Bidirectional LSTM

  • Han Ren
  • Jing WanEmail author
  • Yafeng Ren
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 895)

Abstract

Emotions in cross-lingual text can be expressed in either monolingual or bilingual forms. Current researches have focused on analyzing emotions in monolingual text, whereas such approaches may achieve low performances in the case of identifying emotions in cross-lingual texts, which appear frequently in social media. In this paper, a bidirectional LSTM neural network with emotional knowledge is introduced to detect emotions in cross-lingual texts. This approach also employs the cross-lingual feature and the lexical level feature to analyze texts with multilingual forms and take advantage of emotional knowledge. The evaluation results show that our approach is effective for detecting emotion in cross-lingual texts.

Keywords

Neural network Bidirectional LSTM Emotional knowledge Emotion detection 

Notes

Acknowledgements

This work is supported by Natural Science Foundation of Hainan (618MS086), Special innovation project of Guangdong Education Department (2017KTSCX064), Natural Science Foundation of China (61702121) and Bidding Project of GDUFS Laboratory of Language Engineering and Computing (LEC2016ZBKT002).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Laboratory of Language Engineering and ComputingGuangdong University of Foreign StudiesGuangzhouChina
  2. 2.Center for Lexicographical StudiesGuangdong University of Foreign StudiesGuangzhouChina
  3. 3.Collaborative Innovation Center for Language Research and ServicesGuangdong University of Foreign StudiesGuangzhouChina

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