Random Decision Syntax Trees at SemEval-2018 Task 3: LSTMs and Sentiment Scores for Irony Detection

Aidan San


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
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
560–564
Language:
URL:
https://www.aclweb.org/anthology/S18-1091
DOI:
10.18653/v1/S18-1091
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1091.pdf