Stance detection via sentiment information and neural network model

doi:10.1007/s11704-018-7150-9

Front. Comput. Sci.

2019, Vol. 13

Issue (1) : 127-138 https://doi.org/10.1007/s11704-018-7150-9

RESEARCH ARTICLE

Stance detection via sentiment information and neural network model

Qingying SUN^1,², Zhongqing WANG¹, Shoushan LI¹, Qiaoming ZHU¹(

), Guodong ZHOU¹

¹. Natural Language Processing Lab, Soochow University, Suzhou 215006, China
². Huaiyin Normal University, Huai’an 223300, China

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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 discretemodels 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 detection

Corresponding Author(s): Qiaoming ZHU

Just Accepted Date: 09 October 2017 Online First Date: 06 August 2018 Issue Date: 31 January 2019

Cite this article:

Qingying SUN,Zhongqing WANG,Shoushan LI, et al. Stance detection via sentiment information and neural network model[J]. Front. Comput. Sci., 2019, 13(1): 127-138.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-018-7150-9
https://academic.hep.com.cn/fcs/EN/Y2019/V13/I1/127

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