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dc.contributorDepartment of Computing-
dc.creatorXiang, R-
dc.creatorLu, Q-
dc.creatorJiao, Y-
dc.creatorZheng, Y-
dc.creatorYing, W-
dc.creatorLong, Y-
dc.rights© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en_US
dc.rightsThe following publication Xiang, R., Lu, Q., Jiao, Y. et al. Leveraging writing systems changes for deep learning based Chinese affective analysis. Int. J. Mach. Learn. & Cyber. 10, 3313–3325 (2019) is available at
dc.subjectAffective analysisen_US
dc.subjectDeep learning networken_US
dc.subjectWriting system changesen_US
dc.titleLeveraging writing systems changes for deep learning based Chinese affective analysisen_US
dc.typeJournal/Magazine Articleen_US
dcterms.abstractAffective 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.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationInternational journal of machine learning and cybernetics, 2019, v. 10, no. 11, p. 3313-3325-
dcterms.isPartOfNature communications-
dc.description.validate202001 bcma-
dc.description.oaVersion of Recorden_US
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