EmotiKLUE at IEST 2018: Topic-Informed Classification of Implicit Emotions
Thomas Proisl, Philipp Heinrich, Besim Kabashi, Stefan Evert
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 F₁score of 67.13%, significantly outperforming the baseline produced by a simple ML classifier. Further enhancements after the evaluation period lead to an improved F₁score of 68.10%.- Anthology ID:
- W18-6234
- Volume:
- Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 235–242
- Language:
- URL:
- https://aclanthology.org/W18-6234
- DOI:
- 10.18653/v1/W18-6234
- Cite (ACL):
- Thomas Proisl, Philipp Heinrich, Besim Kabashi, and Stefan Evert. 2018. EmotiKLUE at IEST 2018: Topic-Informed Classification of Implicit Emotions. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 235–242, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- EmotiKLUE at IEST 2018: Topic-Informed Classification of Implicit Emotions (Proisl et al., WASSA 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-6234.pdf
- Code
- tsproisl/EmotiKLUE