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.
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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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Ren, H., Wan, J., Ren, Y. (2020). Emotion Detection in Cross-Lingual Text Based on Bidirectional LSTM. In: Yang, CN., Peng, SL., Jain, L. (eds) Security with Intelligent Computing and Big-data Services. SICBS 2018. Advances in Intelligent Systems and Computing, vol 895. Springer, Cham. https://doi.org/10.1007/978-3-030-16946-6_68
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DOI: https://doi.org/10.1007/978-3-030-16946-6_68
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