UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets

Pavel Přibáň, Tomáš Hercig, Ladislav Lenc


Abstract
This paper describes our system created for the SemEval-2018 Task 1: Affect in Tweets (AIT-2018). We participated in both the regression and the ordinal classification subtasks for emotion intensity detection in English, Arabic, and Spanish. For the regression subtask we use the AffectiveTweets system with added features using various word embeddings, lexicons, and LDA. For the ordinal classification we additionally use our Brainy system with features using parse tree, POS tags, and morphological features. The most beneficial features apart from word and character n-grams include word embeddings, POS count and morphological features.
Anthology ID:
S18-1018
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:
133–140
Language:
URL:
https://www.aclweb.org/anthology/S18-1018
DOI:
10.18653/v1/S18-1018
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1018.pdf