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Efficient Large-Scale Stance Detection in Tweets

Efficient Large-Scale Stance Detection in Tweets

Yilin Yan, Jonathan Chen, Mei-Ling Shyu
Copyright: © 2018 |Volume: 9 |Issue: 3 |Pages: 16
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781522543800|DOI: 10.4018/IJMDEM.2018070101
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MLA

Yan, Yilin, et al. "Efficient Large-Scale Stance Detection in Tweets." IJMDEM vol.9, no.3 2018: pp.1-16. http://doi.org/10.4018/IJMDEM.2018070101

APA

Yan, Y., Chen, J., & Shyu, M. (2018). Efficient Large-Scale Stance Detection in Tweets. International Journal of Multimedia Data Engineering and Management (IJMDEM), 9(3), 1-16. http://doi.org/10.4018/IJMDEM.2018070101

Chicago

Yan, Yilin, Jonathan Chen, and Mei-Ling Shyu. "Efficient Large-Scale Stance Detection in Tweets," International Journal of Multimedia Data Engineering and Management (IJMDEM) 9, no.3: 1-16. http://doi.org/10.4018/IJMDEM.2018070101

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Abstract

Stance detection is an important research direction which attempts to automatically determine the attitude (positive, negative, or neutral) of the author of text (such as tweets), towards a target. Nowadays, a number of frameworks have been proposed using deep learning techniques that show promising results in application domains such as automatic speech recognition and computer vision, as well as natural language processing (NLP). This article shows a novel deep learning-based fast stance detection framework in bipolar affinities on Twitter. It is noted that millions of tweets regarding Clinton and Trump were produced per day on Twitter during the 2016 United States presidential election campaign, and thus it is used as a test use case because of its significant and unique counter-factual properties. In addition, stance detection can be utilized to imply the political tendency of the general public. Experimental results show that the proposed framework achieves high accuracy results when compared to several existing stance detection methods.

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