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        <title>GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection</title>
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    <abstract>The WASSA 2017 EmoInt shared task has the goal to predict emotion intensity values of tweet messages. Given the text of a tweet and its emotion category (anger, joy, fear, and sadness), the participants were asked to build a system that assigns emotion intensity values. Emotion intensity estimation is a challenging problem given the short length of the tweets, the noisy structure of the text and the lack of annotated data. To solve this problem, we developed an ensemble of two neural models, processing input on the character. and word-level with a lexicon-driven system. The correlation scores across all four emotions are averaged to determine the bottom-line competition metric, and our system ranks place forth in full intensity range and third in 0.5-1 range of intensity among 23 systems at the time of writing (June 2017).</abstract>
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%0 Conference Proceedings
%T GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection
%A Lakomkin, Egor
%A Bothe, Chandrakant
%A Wermter, Stefan
%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 lakomkin-etal-2017-gradascent
%X The WASSA 2017 EmoInt shared task has the goal to predict emotion intensity values of tweet messages. Given the text of a tweet and its emotion category (anger, joy, fear, and sadness), the participants were asked to build a system that assigns emotion intensity values. Emotion intensity estimation is a challenging problem given the short length of the tweets, the noisy structure of the text and the lack of annotated data. To solve this problem, we developed an ensemble of two neural models, processing input on the character. and word-level with a lexicon-driven system. The correlation scores across all four emotions are averaged to determine the bottom-line competition metric, and our system ranks place forth in full intensity range and third in 0.5-1 range of intensity among 23 systems at the time of writing (June 2017).
%R 10.18653/v1/W17-5222
%U https://aclanthology.org/W17-5222
%U https://doi.org/10.18653/v1/W17-5222
%P 169-174

Markdown (Informal)

[GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection](https://aclanthology.org/W17-5222) (Lakomkin et al., WASSA 2017)

• GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection (Lakomkin et al., WASSA 2017)

ACL

• Egor Lakomkin, Chandrakant Bothe, and Stefan Wermter. 2017. GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 169–174, Copenhagen, Denmark. Association for Computational Linguistics.