Abstract
Most of the previous emotion classifications are based on binary or ternary classifications, and the final emotion classification results contain only one type of emotion. There is little research on multi-emotional coexistence, which has certain limitations on the restoration of human’s true emotions. Aiming at these deficiencies, this paper proposes a Bidirectional Long-Short Term Memory Multiple Classifiers (BLSTM-MC) model to study the five classification problems in code-switching text, and obtains text contextual relations through BLSTM-MC model. It fully considers the relationship between different emotions in a single post, at the same time, the Attention mechanism is introduced to find the importance of different features and predict all emotions expressed by each post. The model achieved third place in all submissions in the conference NLP&&CC_task1 2018.
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Acknowledgements
This research work is supported by National Natural Science Foundation of China (No. 61402220, No. 61502221), the Philosophy and Social Science Foundation of Hunan Province (No. 16YBA323), the Double First Class Construct Program of USC (2017SYL16), scientific and technological research program of Chongqing municipal education commission (No. KJ1500438), basic and frontier research project of Chongqing, China (No. cstc2015jcyjA40018).
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Wang, T., Yang, X., Ouyang, C., Guo, A., Liu, Y., Li, Z. (2018). A Multi-emotion Classification Method Based on BLSTM-MC in Code-Switching Text. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_16
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