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        <title>A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets</title>
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        <namePart type="given">Javid</namePart>
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    <name type="personal">
        <namePart type="given">Dejing</namePart>
        <namePart type="family">Dou</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>
            <namePart type="family">Prasad</namePart>
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    <abstract>Classifying the stance expressed in online microblogging social media is an emerging problem in opinion mining. We propose a probabilistic approach to stance classification in tweets, which models stance, target of stance, and sentiment of tweet, jointly. Instead of simply conjoining the sentiment or target variables as extra variables to the feature space, we use a novel formulation to incorporate three-way interactions among sentiment-stance-input variables and three-way interactions among target-stance-input variables. The proposed specification intuitively aims to discriminate sentiment features from target features for stance classification. In addition, regularizing a single stance classifier, which handles all targets, acts as a soft weight-sharing among them. We demonstrate that discriminative training of this model achieves the state-of-the-art results in supervised stance classification, and its generative training obtains competitive results in the weakly supervised setting.</abstract>
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%0 Conference Proceedings
%T A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets
%A Ebrahimi, Javid
%A Dou, Dejing
%A Lowd, Daniel
%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 ebrahimi-etal-2016-joint
%X Classifying the stance expressed in online microblogging social media is an emerging problem in opinion mining. We propose a probabilistic approach to stance classification in tweets, which models stance, target of stance, and sentiment of tweet, jointly. Instead of simply conjoining the sentiment or target variables as extra variables to the feature space, we use a novel formulation to incorporate three-way interactions among sentiment-stance-input variables and three-way interactions among target-stance-input variables. The proposed specification intuitively aims to discriminate sentiment features from target features for stance classification. In addition, regularizing a single stance classifier, which handles all targets, acts as a soft weight-sharing among them. We demonstrate that discriminative training of this model achieves the state-of-the-art results in supervised stance classification, and its generative training obtains competitive results in the weakly supervised setting.
%U https://aclanthology.org/C16-1250
%P 2656-2665

Markdown (Informal)

[A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets](https://aclanthology.org/C16-1250) (Ebrahimi et al., COLING 2016)

• A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets (Ebrahimi et al., COLING 2016)

ACL

• Javid Ebrahimi, Dejing Dou, and Daniel Lowd. 2016. A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2656–2665, Osaka, Japan. The COLING 2016 Organizing Committee.