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        <title>Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets</title>
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        <namePart type="given">Pranav</namePart>
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        <namePart type="given">Devang</namePart>
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    <name type="personal">
        <namePart type="given">Kaushal</namePart>
        <namePart type="given">Kumar</namePart>
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            <title>Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets
%A Goel, Pranav
%A Kulshreshtha, Devang
%A Jain, Prayas
%A Shukla, Kaushal Kumar
%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 goel-etal-2017-prayas
%X 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.
%R 10.18653/v1/W17-5207
%U https://aclanthology.org/W17-5207
%U https://doi.org/10.18653/v1/W17-5207
%P 58-65

Markdown (Informal)

[Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets](https://aclanthology.org/W17-5207) (Goel et al., WASSA 2017)

• Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets (Goel et al., WASSA 2017)

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.