Abstract
In this work, we investigate the impact of incorporating emotion classes on the task of predicting emojis from Twitter texts. More specifically, we first show that there is a correlation between the emotion expressed in the text and the emoji choice of Twitter users. Based on this insight we propose a few simple methods to incorporate emotion information in traditional classifiers. Through automatic metrics, human evaluation, and error analysis, we show that the improvement obtained by incorporating emotion is significant and correlate better with human preferences compared to the baseline models. Through the human ratings that we obtained, we also argue for preference metric to better evaluate the usefulness of an emoji prediction system.- Anthology ID:
- W19-1311
- Volume:
- Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, USA
- Editors:
- Alexandra Balahur, Roman Klinger, Veronique Hoste, Carlo Strapparava, Orphee De Clercq
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 91–99
- Language:
- URL:
- https://aclanthology.org/W19-1311
- DOI:
- 10.18653/v1/W19-1311
- Cite (ACL):
- Shirley Anugrah Hayati and Aldrian Obaja Muis. 2019. Analyzing Incorporation of Emotion in Emoji Prediction. In Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 91–99, Minneapolis, USA. Association for Computational Linguistics.
- Cite (Informal):
- Analyzing Incorporation of Emotion in Emoji Prediction (Hayati & Muis, WASSA 2019)
- PDF:
- https://preview.aclanthology.org/autopr/W19-1311.pdf