@inproceedings{witon-etal-2018-disney,
title = "Disney at {IEST} 2018: Predicting Emotions using an Ensemble",
author = "Witon, Wojciech and
Colombo, Pierre and
Modi, Ashutosh and
Kapadia, Mubbasir",
editor = "Balahur, Alexandra and
Mohammad, Saif M. and
Hoste, Veronique and
Klinger, Roman",
booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6236",
doi = "10.18653/v1/W18-6236",
pages = "248--253",
abstract = "This paper describes our participating system in the WASSA 2018 shared task on emotion prediction. The task focuses on implicit emotion prediction in a tweet. In this task, keywords corresponding to the six emotion labels used (anger, fear, disgust, joy, sad, and surprise) have been removed from the tweet text, making emotion prediction implicit and the task challenging. We propose a model based on an ensemble of classifiers for prediction. Each classifier uses a sequence of Convolutional Neural Network (CNN) architecture blocks and uses ELMo (Embeddings from Language Model) as an input. Our system achieves a 66.2{\%} F1 score on the test set. The best performing system in the shared task has reported a 71.4{\%} F1 score.",
}
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<abstract>This paper describes our participating system in the WASSA 2018 shared task on emotion prediction. The task focuses on implicit emotion prediction in a tweet. In this task, keywords corresponding to the six emotion labels used (anger, fear, disgust, joy, sad, and surprise) have been removed from the tweet text, making emotion prediction implicit and the task challenging. We propose a model based on an ensemble of classifiers for prediction. Each classifier uses a sequence of Convolutional Neural Network (CNN) architecture blocks and uses ELMo (Embeddings from Language Model) as an input. Our system achieves a 66.2% F1 score on the test set. The best performing system in the shared task has reported a 71.4% F1 score.</abstract>
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%0 Conference Proceedings
%T Disney at IEST 2018: Predicting Emotions using an Ensemble
%A Witon, Wojciech
%A Colombo, Pierre
%A Modi, Ashutosh
%A Kapadia, Mubbasir
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y Hoste, Veronique
%Y Klinger, Roman
%S Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F witon-etal-2018-disney
%X This paper describes our participating system in the WASSA 2018 shared task on emotion prediction. The task focuses on implicit emotion prediction in a tweet. In this task, keywords corresponding to the six emotion labels used (anger, fear, disgust, joy, sad, and surprise) have been removed from the tweet text, making emotion prediction implicit and the task challenging. We propose a model based on an ensemble of classifiers for prediction. Each classifier uses a sequence of Convolutional Neural Network (CNN) architecture blocks and uses ELMo (Embeddings from Language Model) as an input. Our system achieves a 66.2% F1 score on the test set. The best performing system in the shared task has reported a 71.4% F1 score.
%R 10.18653/v1/W18-6236
%U https://aclanthology.org/W18-6236
%U https://doi.org/10.18653/v1/W18-6236
%P 248-253
Markdown (Informal)
[Disney at IEST 2018: Predicting Emotions using an Ensemble](https://aclanthology.org/W18-6236) (Witon et al., WASSA 2018)
ACL
- Wojciech Witon, Pierre Colombo, Ashutosh Modi, and Mubbasir Kapadia. 2018. Disney at IEST 2018: Predicting Emotions using an Ensemble. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 248–253, Brussels, Belgium. Association for Computational Linguistics.