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Stance detection via sentiment information and neural network model

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Abstract

Stance detection aims to automatically determine whether the author is in favor of or against a given target. In principle, the sentiment information of a post highly influences the stance. In this study, we aim to leverage the sentiment information of a post to improve the performance of stance detection. However, conventional discrete models with sentimental features can cause error propagation. We thus propose a joint neural network model to predict the stance and sentiment of a post simultaneously, because the neural network model can learn both representation and interaction between the stance and sentiment collectively. Specifically, we first learn a deep shared representation between stance and sentiment information, and then use a neural stacking model to leverage sentimental information for the stance detection task. Empirical studies demonstrate the effectiveness of our proposed joint neural model.

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Acknowledgements

In building our system, we are grateful to all the people who have helped us. Jingjing Wang, Lu Zhang, Dong Zhang, and Suyang Zhu have provided help in programing; Zhenghua Li, Xing Wang, and Ziwei Fan have given us insightful comments etc. We also would like to thank the organizer of SemEval-2016 Task 6 for the hard work, especially in data annotation.

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61331011, 61751206, 61773276, 61672366), Jiangsu Provincial Science and Technology Plan (BK20151222), Project of Natural Science Research of the Universities of Jiangsu Province (16KJB520007) and Huaiyin Normal University Youth Talent Support Program (13HSQNZ07).

Author information

Authors and Affiliations

  1. Natural Language Processing Lab, Soochow University, Suzhou, 215006, China

    Qingying Sun, Zhongqing Wang, Shoushan Li, Qiaoming Zhu & Guodong Zhou

  2. Huaiyin Normal University, Huai’an, 223300, China

    Qingying Sun

Authors
  1. Qingying Sun
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  2. Zhongqing Wang
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  3. Shoushan Li
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  4. Qiaoming Zhu
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  5. Guodong Zhou
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Corresponding author

Correspondence to Qiaoming Zhu.

Additional information

Qingying Sun received the Master’s degree in July 2010 from the School of Computer Science and Technology, Nanjing University of Science & Technology, China. Since 2015, she has been a PhD candidate in the School of Computer Science and Technology, Soochow University, China. Her current research interests include natural language language processing, sentiment analysis, stance detection and social computing.

Zhongqing Wang received his PhD degree in 2016 from the School of Computer Science and Technology, Soochow University, China. Since April 2016, he has been a postdoctoral research fellow at Singapore University of Technology and Design, Singapore. He is a lecturer in the School of Computer Science and Technology, Soochow University, China. His current research interests include natural language processing, sentiment analysis and social computing.

Shoushan Li received his PhD degree in 2008 from National Laboratory of Pattern Recognition, CASIA, China. He is a full professor in the School of Computer Science and Technology, Soochow University, China. His current research interests include natural language processing, social computing, and sentiment analysis.

Qiaoming Zhu received his PhD degree in 2006 from the School of Computer Science and Technology, Soochow University, China. He is a full professor in the School of Computer Science and Technology. His research interests include natural language processing, sentiment analysis, discourse analysis.

Guodong Zhou received his PhD degree in 1999 from the National University of Singapore, Singapore. He is a full professor in the School of Computer Science and Technology, and the Director of the Natural Language Processing Laboratory from Soochow University, China. His research interests include information retrieval, natural language processing.

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Sun, Q., Wang, Z., Li, S. et al. Stance detection via sentiment information and neural network model. Front. Comput. Sci. 13, 127–138 (2019). https://doi.org/10.1007/s11704-018-7150-9

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