Abstract
The paper describes the best performing system for the SemEval-2018 Affect in Tweets(English) sub-tasks. The system focuses on the ordinal classification and regression sub-tasks for valence and emotion. For ordinal classification valence is classified into 7 different classes ranging from -3 to 3 whereas emotion is classified into 4 different classes 0 to 3 separately for each emotion namely anger, fear, joy and sadness. The regression sub-tasks estimate the intensity of valence and each emotion. The system performs domain adaptation of 4 different models and creates an ensemble to give the final prediction. The proposed system achieved 1stposition out of 75 teams which participated in the fore-mentioned sub-tasks. We outperform the baseline model by margins ranging from 49.2% to 76.4 %, thus, pushing the state-of-the-art significantly.- Anthology ID:
- S18-1002
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venues:
- SemEval | *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 18–23
- Language:
- URL:
- https://aclanthology.org/S18-1002
- DOI:
- 10.18653/v1/S18-1002
- Cite (ACL):
- Venkatesh Duppada, Royal Jain, and Sushant Hiray. 2018. SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 18–23, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets (Duppada et al., SemEval-*SEM 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/S18-1002.pdf