@inproceedings{r-etal-2017-deepcybernet,
title = "deep{C}yb{E}r{N}et at {E}mo{I}nt-2017: Deep Emotion Intensities in Tweets",
author = "R, Vinayakumar and
B, Premjith and
S, Sachin Kumar and
KP, Soman and
Poornachandran, Prabaharan",
editor = "Balahur, Alexandra and
Mohammad, Saif M. and
van der Goot, Erik",
booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5237",
doi = "10.18653/v1/W17-5237",
pages = "259--263",
abstract = "This working note presents the methodology used in deepCybErNet submission to the shared task on Emotion Intensities in Tweets (EmoInt) WASSA-2017. The goal of the task is to predict a real valued score in the range [0-1] for a particular tweet with an emotion type. To do this, we used Bag-of-Words and embedding based on recurrent network architecture. We have developed two systems and experiments are conducted on the Emotion Intensity shared Task 1 data base at WASSA-2017. A system which uses word embedding based on recurrent network architecture has achieved highest 5 fold cross-validation accuracy. This has used embedding with recurrent network to extract optimal features at tweet level and logistic regression for prediction. These methods are highly language independent and experimental results shows that the proposed methods are apt for predicting a real valued score in than range [0-1] for a given tweet with its emotion type.",
}
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<abstract>This working note presents the methodology used in deepCybErNet submission to the shared task on Emotion Intensities in Tweets (EmoInt) WASSA-2017. The goal of the task is to predict a real valued score in the range [0-1] for a particular tweet with an emotion type. To do this, we used Bag-of-Words and embedding based on recurrent network architecture. We have developed two systems and experiments are conducted on the Emotion Intensity shared Task 1 data base at WASSA-2017. A system which uses word embedding based on recurrent network architecture has achieved highest 5 fold cross-validation accuracy. This has used embedding with recurrent network to extract optimal features at tweet level and logistic regression for prediction. These methods are highly language independent and experimental results shows that the proposed methods are apt for predicting a real valued score in than range [0-1] for a given tweet with its emotion type.</abstract>
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%0 Conference Proceedings
%T deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets
%A R, Vinayakumar
%A B, Premjith
%A S, Sachin Kumar
%A KP, Soman
%A Poornachandran, Prabaharan
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y van der Goot, Erik
%S Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F r-etal-2017-deepcybernet
%X This working note presents the methodology used in deepCybErNet submission to the shared task on Emotion Intensities in Tweets (EmoInt) WASSA-2017. The goal of the task is to predict a real valued score in the range [0-1] for a particular tweet with an emotion type. To do this, we used Bag-of-Words and embedding based on recurrent network architecture. We have developed two systems and experiments are conducted on the Emotion Intensity shared Task 1 data base at WASSA-2017. A system which uses word embedding based on recurrent network architecture has achieved highest 5 fold cross-validation accuracy. This has used embedding with recurrent network to extract optimal features at tweet level and logistic regression for prediction. These methods are highly language independent and experimental results shows that the proposed methods are apt for predicting a real valued score in than range [0-1] for a given tweet with its emotion type.
%R 10.18653/v1/W17-5237
%U https://aclanthology.org/W17-5237
%U https://doi.org/10.18653/v1/W17-5237
%P 259-263
Markdown (Informal)
[deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets](https://aclanthology.org/W17-5237) (R et al., WASSA 2017)
ACL
- Vinayakumar R, Premjith B, Sachin Kumar S, Soman KP, and Prabaharan Poornachandran. 2017. deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 259–263, Copenhagen, Denmark. Association for Computational Linguistics.