Stance Classification of Twitter Debates

Author:

Addawood Aseel1,Schneider Jodi2,Bashir Masooda2

Affiliation:

1. Illinois Informatics Institute, University of Illinois at Urbana-Champaign

2. School of Information Sciences, University of Illinois at Urbana-Champaign

Publisher

ACM Press

Reference41 articles.

1. Anatoliy Gruzd and Melissa Goertzen. 2013. Wired academia: Why social science scholars are using social media. In Proceedings of the 2013 46th Hawaii International Conference on System Sciences (HICSS). IEEE, 3332--3341.

2. Wei Wang, Ivan Hernandez, Daniel A Newman, Jibo He and Jiang Bian.2016.Twitter analysis: Studying US weekly trends in work stress and emotion. Applied Psychology, 65, 2 (2016), 355--378.

3. Bo Pang, Lillian Lee and Shivakumar Vaithyanathan. 2002. Thumbs up?: Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing. ACL, 79--86.

4. Saif M Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu and Colin Cherry. 2016. Semeval-2016 Task 6: Detecting stance in tweets. Proceedings of SemEval, 16 (2016).

5. Amjad Abu-Jbara, Mona Diab, Pradeep Dasigi and Dragomir Radev. 2012. Subgroup detection in ideological discussions. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. ACL, 399--409.

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