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Target-specific Convolutional \ Bi-directional LSTM Neural Network for \Political Ideology Analysis
LI Xi-Lian, CHEN Wei, WANG Teng-Jiao
School of Electronics Engineering and Computer Science, Peking University, Beijing 100871
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Funding: Specialized Research Fund for the Doctoral Program of Higher Education （No.Grant No.20130001120001）
Opened online:26 April 2017
Accepted by: none
Citation: LI Xi-Lian, CHEN Wei, WANG Teng-Jiao.Target-specific Convolutional \ Bi-directional LSTM Neural Network for \Political Ideology Analysis[OL]. [26 April 2017] http://en.paper.edu.cn/en_releasepaper/content/4726068
 
 
Ideology detection from text plays an important role in identifying the political ideology of politicians who have expressed their beliefs on many issues. Most existing approaches based on bag-of-words features fail to capture semantic information. And other sentence modeling methods are inefficient to extract ideological target context which is significant for identifying the political ideology. In this paper, we propose a target-specific Convolutional and Bi-directional Long Short Term Memory neural network (CB-LSTM) which is expert in intensifying ideological target-related context and learning semantic representations of the text at the same time. We conduct experiments on two commonly used datasets and a well-designed twitter dataset. The experimental results show that the CB-LSTM outperforms the state-of-the-art methods.
Keywords:Ideology Detection, Ideological Target, Convolutional neural network, Recurrent neural network
 
 
 

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