Sentence-level emotion classification with label and context dependence?

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Abstract

Predicting emotion categories, such as anger, joy, and anxiety, expressed by a sentence is challenging due to its inherent multi-label classification difficulty and data sparseness. In this paper, we address above two challenges by incorporating the label dependence among the emotion labels and the context dependence among the contextual instances into a factor graph model. Specifically, we recast sentence-level emotion classification as a factor graph inferring problem in which the label and context dependence are modeled as various factor functions. Empirical evaluation demonstrates the great potential and effectiveness of our proposed approach to sentencelevel emotion classification.

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Li, S., Huang, L., Wang, R., & Zhou, G. (2015). Sentence-level emotion classification with label and context dependence? In ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference (Vol. 1, pp. 1045–1053). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/p15-1101

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