Journal
FRONTIERS OF COMPUTER SCIENCE
Volume 13, Issue 1, Pages 127-138Publisher
HIGHER EDUCATION PRESS
DOI: 10.1007/s11704-018-7150-9
Keywords
natural language processing; machine learning; stance detection
Categories
Funding
- National Natural Science Foundation of China [61331011, 61751206, 61773276, 61672366]
- Jiangsu Provincial Science and Technology Plan [BK20151222]
- Project of Natural Science Research of the Universities of Jiangsu Province [16KJB520007]
- Huaiyin Normal University Youth Talent Support Program [13HSQNZ07]
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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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