NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity

Vladimir Andryushechkin, Ian Wood, James O’ Neill


Abstract
This paper describes the entry NUIG in the WASSA 2017 (8th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis) shared task on emotion recognition. The NUIG system used an SVR (SVM regression) and BLSTM ensemble, utilizing primarily n-grams (for SVR features) and tweet word embeddings (for BLSTM features). Experiments were carried out on several other candidate features, some of which were added to the SVR model. Parameter selection for the SVR model was run as a grid search whilst parameters for the BLSTM model were selected through a non-exhaustive ad-hoc search.
Anthology ID:
W17-5223
Volume:
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venues:
WASSA | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
175–179
Language:
URL:
https://aclanthology.org/W17-5223
DOI:
10.18653/v1/W17-5223
Bibkey:
Cite (ACL):
Vladimir Andryushechkin, Ian Wood, and James O’ Neill. 2017. NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 175–179, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
NUIG at EmoInt-2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity (Andryushechkin et al., 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-5223.pdf