Abstract
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 suitable 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 dataset extracted from tweets. The experimental results show that the proposed method outperforms the state-of-the-art methods.
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Acknowledgments
This research is supported by the Natural Science Foundation of China (Grant No. 61572043) and National Key Research and Development Program (Project Number: 2016YFB1000704).
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Li, X., Chen, W., Wang, T., Huang, W. (2017). Target-Specific Convolutional Bi-directional LSTM Neural Network for Political Ideology Analysis. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_5
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DOI: https://doi.org/10.1007/978-3-319-63564-4_5
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