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        <title>YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model</title>
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        <namePart type="given">Yuanye</namePart>
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    <name type="personal">
        <namePart type="given">Liang-Chih</namePart>
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    <name type="personal">
        <namePart type="given">Weiyi</namePart>
        <namePart type="family">Liu</namePart>
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            <namePart type="given">Erik</namePart>
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    <abstract>The EmoInt-2017 task aims to determine a continuous numerical value representing the intensity to which an emotion is expressed in a tweet. Compared to classification tasks that identify 1 among n emotions for a tweet, the present task can provide more fine-grained (real-valued) sentiment analysis. This paper presents a system that uses a bi-directional LSTM-CNN model to complete the competition task. Combining bi-directional LSTM and CNN, the prediction process considers both global information in a tweet and local important information. The proposed method ranked sixth among twenty-one teams in terms of Pearson Correlation Coefficient.</abstract>
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%0 Conference Proceedings
%T YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model
%A He, Yuanye
%A Yu, Liang-Chih
%A Lai, K. Robert
%A Liu, Weiyi
%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 he-etal-2017-yzu
%X The EmoInt-2017 task aims to determine a continuous numerical value representing the intensity to which an emotion is expressed in a tweet. Compared to classification tasks that identify 1 among n emotions for a tweet, the present task can provide more fine-grained (real-valued) sentiment analysis. This paper presents a system that uses a bi-directional LSTM-CNN model to complete the competition task. Combining bi-directional LSTM and CNN, the prediction process considers both global information in a tweet and local important information. The proposed method ranked sixth among twenty-one teams in terms of Pearson Correlation Coefficient.
%R 10.18653/v1/W17-5233
%U https://aclanthology.org/W17-5233
%U https://doi.org/10.18653/v1/W17-5233
%P 238-242

Markdown (Informal)

[YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model](https://aclanthology.org/W17-5233) (He et al., WASSA 2017)

• YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model (He et al., WASSA 2017)

ACL

• Yuanye He, Liang-Chih Yu, K. Robert Lai, and Weiyi Liu. 2017. YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 238–242, Copenhagen, Denmark. Association for Computational Linguistics.