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.
Keywords
natural language processing machine learning stance detectionPreview
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Notes
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).
Supplementary material
11704_2018_7150_MOESM1_ESM.ppt (204 kb)
Stance Detection via Sentiment Information and Neural Network Model
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