Xiang, Rong and Lu, Qin and Jiao, Ying and Zheng, Yufei and Ying, Wenhao and Long, Yunfei (2019) Leveraging writing systems changes for deep learning based Chinese affective analysis. International Journal of Machine Learning and Cybernetics, 10 (11). pp. 3313-3325. DOI https://doi.org/10.1007/s13042-019-01019-z
Xiang, Rong and Lu, Qin and Jiao, Ying and Zheng, Yufei and Ying, Wenhao and Long, Yunfei (2019) Leveraging writing systems changes for deep learning based Chinese affective analysis. International Journal of Machine Learning and Cybernetics, 10 (11). pp. 3313-3325. DOI https://doi.org/10.1007/s13042-019-01019-z
Xiang, Rong and Lu, Qin and Jiao, Ying and Zheng, Yufei and Ying, Wenhao and Long, Yunfei (2019) Leveraging writing systems changes for deep learning based Chinese affective analysis. International Journal of Machine Learning and Cybernetics, 10 (11). pp. 3313-3325. DOI https://doi.org/10.1007/s13042-019-01019-z
Abstract
Affective analysis of social media text is in great demand. Online text written in Chinese communities often contains mixed scripts including major text written in Chinese, an ideograph-based writing system, and minor text using Latin letters, an alphabet-based writing system. This phenomenon is referred to as writing systems changes (WSCs). Past studies have shown that WSCs often reflect unfiltered immediate affections. However, the use of WSCs poses more challenges in Natural Language Processing tasks because WSCs can break the syntax of the major text. In this work, we present our work to use WSCs as an effective feature in a hybrid deep learning model with attention network. The WSCs scripts are first identified by their encoding range. Then, the document representation of the text is learned through a Long Short-Term Memory model and the minor text is learned by a separate Convolution Neural Network model. To further highlight the WSCs components, an attention mechanism is adopted to re-weight the feature vector before the classification layer. Experiments show that the proposed hybrid deep learning method which better incorporates WSCs features can further improve performance compared to the state-of-the-art classification models. The experimental result indicates that WSCs can serve as effective information in affective analysis of the social media text.
Item Type: | Article |
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Uncontrolled Keywords: | Affective analysis; Writing system changes; Deep learning network |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 01 Jun 2020 17:18 |
Last Modified: | 30 Oct 2024 20:33 |
URI: | http://repository.essex.ac.uk/id/eprint/27751 |
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Filename: Xiang2019_Article_LeveragingWritingSystemsChange.pdf
Licence: Creative Commons: Attribution 3.0