Abstract
In this paper we describe our submission to SemEval-2018 Task 1: Affects in Tweets. The model which we present is an ensemble of various neural architectures and gradient boosted trees, and employs three different types of vectorial tweet representations. Furthermore, our system is language-independent and ranked first in 5 out of the 12 subtasks in which we participated, while achieving competitive results in the remaining ones. Comparatively remarkable performance is observed on both the Arabic and Spanish languages.- Anthology ID:
- S18-1032
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 210–217
- Language:
- URL:
- https://aclanthology.org/S18-1032
- DOI:
- 10.18653/v1/S18-1032
- Cite (ACL):
- Mostafa Abdou, Artur Kulmizev, and Joan Ginés i Ametllé. 2018. AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 210–217, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets (Abdou et al., SemEval 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S18-1032.pdf