Abstract
We propose a Long Short Term Memory Neural Network model for irony detection in tweets in this paper. Our model is trained using word embeddings and emoji embeddings. We show that adding sentiment scores to our model improves the F1 score of our baseline LSTM by approximately .012, and therefore show that high-level features can be used to improve word embeddings in certain Natural Language Processing applications. Our model ranks 24/43 for binary classification and 5/31 for multiclass classification. We make our model easily accessible to the research community.- Anthology ID:
- S18-1091
- 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:
- 560–564
- Language:
- URL:
- https://aclanthology.org/S18-1091
- DOI:
- 10.18653/v1/S18-1091
- Cite (ACL):
- Aidan San. 2018. Random Decision Syntax Trees at SemEval-2018 Task 3: LSTMs and Sentiment Scores for Irony Detection. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 560–564, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Random Decision Syntax Trees at SemEval-2018 Task 3: LSTMs and Sentiment Scores for Irony Detection (San, SemEval 2018)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/S18-1091.pdf