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    <titleInfo>
        <title>UTCNN: a Deep Learning Model of Stance Classification on Social Media Text</title>
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    <name type="personal">
        <namePart type="given">Wei-Fan</namePart>
        <namePart type="family">Chen</namePart>
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    <name type="personal">
        <namePart type="given">Lun-Wei</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <abstract>Most neural network models for document classification on social media focus on text information to the neglect of other information on these platforms. In this paper, we classify post stance on social media channels and develop UTCNN, a neural network model that incorporates user tastes, topic tastes, and user comments on posts. UTCNN not only works on social media texts, but also analyzes texts in forums and message boards. Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0.755 macro average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld. This model design greatly mitigates the lack of data for the minor class. In addition, UTCNN yields a 0.842 accuracy on English online debate forum data, which also significantly outperforms results from previous work, showing that UTCNN performs well regardless of language or platform.</abstract>
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%0 Conference Proceedings
%T UTCNN: a Deep Learning Model of Stance Classification on Social Media Text
%A Chen, Wei-Fan
%A Ku, Lun-Wei
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F chen-ku-2016-utcnn
%X Most neural network models for document classification on social media focus on text information to the neglect of other information on these platforms. In this paper, we classify post stance on social media channels and develop UTCNN, a neural network model that incorporates user tastes, topic tastes, and user comments on posts. UTCNN not only works on social media texts, but also analyzes texts in forums and message boards. Experiments performed on Chinese Facebook data and English online debate forum data show that UTCNN achieves a 0.755 macro average f-score for supportive, neutral, and unsupportive stance classes on Facebook data, which is significantly better than models in which either user, topic, or comment information is withheld. This model design greatly mitigates the lack of data for the minor class. In addition, UTCNN yields a 0.842 accuracy on English online debate forum data, which also significantly outperforms results from previous work, showing that UTCNN performs well regardless of language or platform.
%U https://aclanthology.org/C16-1154
%P 1635-1645

Markdown (Informal)

[UTCNN: a Deep Learning Model of Stance Classification on Social Media Text](https://aclanthology.org/C16-1154) (Chen & Ku, COLING 2016)

• UTCNN: a Deep Learning Model of Stance Classification on Social Media Text (Chen & Ku, COLING 2016)

ACL

• Wei-Fan Chen and Lun-Wei Ku. 2016. UTCNN: a Deep Learning Model of Stance Classification on Social Media Text. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1635–1645, Osaka, Japan. The COLING 2016 Organizing Committee.