A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets

Javid Ebrahimi, Dejing Dou, Daniel Lowd


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.
Anthology ID:
C16-1250
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2656–2665
Language:
URL:
https://aclanthology.org/C16-1250
DOI:
Bibkey:
Cite (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.
Cite (Informal):
A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets (Ebrahimi et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-1250.pdf