LT3 at SemEval-2018 Task 1: A classifier chain to detect emotions in tweets

Luna De Bruyne, Orphée De Clercq, Véronique Hoste


Abstract
This paper presents an emotion classification system for English tweets, submitted for the SemEval shared task on Affect in Tweets, subtask 5: Detecting Emotions. The system combines lexicon, n-gram, style, syntactic and semantic features. For this multi-class multi-label problem, we created a classifier chain. This is an ensemble of eleven binary classifiers, one for each possible emotion category, where each model gets the predictions of the preceding models as additional features. The predicted labels are combined to get a multi-label representation of the predictions. Our system was ranked eleventh among thirty five participating teams, with a Jaccard accuracy of 52.0% and macro- and micro-average F1-scores of 49.3% and 64.0%, respectively.
Anthology ID:
S18-1016
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:
123–127
Language:
URL:
https://www.aclweb.org/anthology/S18-1016
DOI:
10.18653/v1/S18-1016
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1016.pdf