Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets

Pranav Goel, Devang Kulshreshtha, Prayas Jain, Kaushal Kumar Shukla


Abstract
The paper describes the best performing system for EmoInt - a shared task to predict the intensity of emotions in tweets. Intensity is a real valued score, between 0 and 1. The emotions are classified as - anger, fear, joy and sadness. We apply three different deep neural network based models, which approach the problem from essentially different directions. Our final performance quantified by an average pearson correlation score of 74.7 and an average spearman correlation score of 73.5 is obtained using an ensemble of the three models. We outperform the baseline model of the shared task by 9.9% and 9.4% pearson and spearman correlation scores respectively.
Anthology ID:
W17-5207
Volume:
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Alexandra Balahur, Saif M. Mohammad, Erik van der Goot
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
58–65
Language:
URL:
https://aclanthology.org/W17-5207
DOI:
10.18653/v1/W17-5207
Bibkey:
Cite (ACL):
Pranav Goel, Devang Kulshreshtha, Prayas Jain, and Kaushal Kumar Shukla. 2017. Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 58–65, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets (Goel et al., WASSA 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-5207.pdf