Papers › EmotiKLUE at IEST 2018: Topic-Informed Classification of Implicit Emotions
EmotiKLUE at IEST 2018: Topic-Informed Classification of Implicit Emotions
Thomas Proisl, Philipp Heinrich, Besim Kabashi, Stefan Evert
EmotiKLUE is a submission to the Implicit Emotion Shared Task. It is a deep learning system that combines independent representations of the left and right contexts of the emotion word with the topic distribution of an LDA topic model. EmotiKLUE achieves a macro average \textit{F₁}score of 67.13{\%}, significantly outperforming the baseline produced by a simple ML classifier. Further enhancements after the evaluation period lead to an improved \textit{F₁}score of 68.10{\%}.
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