Abstract
The paper describes experiments on estimating emotion intensity in tweets using a generalized regressor system. The system combines various independent feature extractors, trains them on general regressors and finally combines the best performing models to create an ensemble. The proposed system stood 3rd out of 22 systems in leaderboard of WASSA-2017 Shared Task on Emotion Intensity.- Anthology ID:
- W17-5228
- Volume:
- Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Alexandra Balahur, Saif M. Mohammad, Erik van der Goot
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 205–211
- Language:
- URL:
- https://aclanthology.org/W17-5228
- DOI:
- 10.18653/v1/W17-5228
- Cite (ACL):
- Venkatesh Duppada and Sushant Hiray. 2017. Seernet at EmoInt-2017: Tweet Emotion Intensity Estimator. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 205–211, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Seernet at EmoInt-2017: Tweet Emotion Intensity Estimator (Duppada & Hiray, WASSA 2017)
- PDF:
- https://preview.aclanthology.org/naacl24-info/W17-5228.pdf
- Code
- SEERNET/EmoInt