Abstract
Existing studies on emotion recognition in conversations have generally analyzed and classified emotions based on a speaker’s utterances from conversations. On the other hand, little research has predicted emotions without such speaker utterances. In this study, we propose an emotion prediction model that forecasts a speaker’s emotion before she makes a statement utilizing conversational context and commonsense knowledge graphs. In an evaluation experiment, we rate our proposed model’s performance using an emotion recognition dataset in conversations.
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Acknowledgements
This work was supported by JST CREST Grant Number JPMJCR20D1, Japan.
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Fujimoto, T., Ito, T. (2023). Emotion Prediction Based on Conversational Context and Commonsense Knowledge Graphs. In: Fujita, H., Wang, Y., Xiao, Y., Moonis, A. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2023. Lecture Notes in Computer Science(), vol 13925. Springer, Cham. https://doi.org/10.1007/978-3-031-36819-6_36
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DOI: https://doi.org/10.1007/978-3-031-36819-6_36
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