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EmotiKLUE at IEST 2018: Topic-Informed Classification of Implicit Emotions

Proisl T, Heinrich P, Kabashi B, Evert S (2018)

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Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2018

Publisher: Association for Computational Linguistics

City/Town: Brussels

Pages Range: 235–242

Conference Proceedings Title: Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Event location: Brüssel

URI: http://aclweb.org/anthology/W18-6234

DOI: 10.18653/v1/w18-6234

Open Access Link: http://aclweb.org/anthology/W18-6234

Abstract

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 F 1 score of 67.13%, significantly outperforming the baseline produced by a simple ML classifier. Further enhancements after the evaluation period lead to an improved F 1 score of 68.10%

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How to cite

APA:

Proisl, T., Heinrich, P., Kabashi, B., & Evert, S. (2018). EmotiKLUE at IEST 2018: Topic-Informed Classification of Implicit Emotions. In Balahur A, Mohammad SM, Hoste V, Klinger R (Eds.), Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (pp. 235–242). Brüssel, BE: Brussels: Association for Computational Linguistics.

MLA:

Proisl, Thomas, et al. "EmotiKLUE at IEST 2018: Topic-Informed Classification of Implicit Emotions." Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Brüssel Ed. Balahur A, Mohammad SM, Hoste V, Klinger R, Brussels: Association for Computational Linguistics, 2018. 235–242.

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