{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T22:48:51Z","timestamp":1757544531420,"version":"3.37.3"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T00:00:00Z","timestamp":1570665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T00:00:00Z","timestamp":1570665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004377","name":"Hong Kong Polytechnic University","doi-asserted-by":"publisher","award":["PolyU RTVU","152006\/16E"],"award-info":[{"award-number":["PolyU RTVU","152006\/16E"]}],"id":[{"id":"10.13039\/501100004377","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Nottingham Biomedical Research Centre","award":["RC48ES"],"award-info":[{"award-number":["RC48ES"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2019,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n              <jats:p>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.<\/jats:p>","DOI":"10.1007\/s13042-019-01019-z","type":"journal-article","created":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T10:19:32Z","timestamp":1570702772000},"page":"3313-3325","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging writing systems changes for deep learning based Chinese affective analysis"],"prefix":"10.1007","volume":"10","author":[{"given":"Rong","family":"Xiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qin","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufei","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhao","family":"Ying","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4407-578X","authenticated-orcid":false,"given":"Yunfei","family":"Long","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"1019_CR1","unstructured":"Barbosa L, Feng J (2010) Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of the 23rd international conference on computational linguistics: posters, Association for Computational Linguistics, pp 36\u201344"},{"key":"1019_CR2","unstructured":"Balamurali A, Joshi A, Bhattacharyya P (2011) Harnessing wordnet senses for supervised sentiment classification. In: Proceedings of the conference on empirical methods in natural language processing, Association for Computational Linguistics, pp 1081\u20131091"},{"key":"1019_CR3","doi-asserted-by":"crossref","unstructured":"Liu B, Zhang L (2012) A survey of opinion mining and sentiment analysis. In: Mining text data, Springer, pp 415\u2013463","DOI":"10.1007\/978-1-4614-3223-4_13"},{"key":"1019_CR4","unstructured":"Wilson T, Kozareva Z, Nakov P, Rosenthal S, Stoyanov V, Ritter A (2013) Sentiment analysis in twitter. In: Proceedings of the international workshop on semantic"},{"key":"1019_CR5","first-page":"5422","volume":"5","author":"NS Joshi","year":"2014","unstructured":"Joshi NS, Itkat SA (2014) A survey on feature level sentiment analysis. Int J Comput Sci Inf Technol 5:5422\u20135425","journal-title":"Int J Comput Sci Inf Technol"},{"key":"1019_CR6","doi-asserted-by":"crossref","unstructured":"Mishra A, Dey K, Bhattacharyya P (2017) Learning cognitive features from gaze data for sentiment and sarcasm classification using convolutional neural network. In: Proceedings of the 55th annual meeting of the association for computational linguistics (volume 1: long papers), vol 1, pp 377\u2013387","DOI":"10.18653\/v1\/P17-1035"},{"key":"1019_CR7","unstructured":"Kanter JM, Veeramachaneni K (2015) Deep feature synthesis: towards automating data science endeavors. In: Data science and advanced analytics (DSAA), 2015. 36678 2015. IEEE International Conference on, IEEE, pp 1\u201310"},{"key":"1019_CR8","unstructured":"Dos\u00a0Santos CN, Gatti M (2014) Deep convolutional neural networks for sentiment analysis of short texts. In: COLING, pp 69\u201378"},{"key":"1019_CR9","unstructured":"Clyne M (2000) Constraints on code-switching: How universal are they. The bilingualism reader, pp 257\u2013280"},{"key":"1019_CR10","doi-asserted-by":"publisher","DOI":"10.1515\/9783110809121","volume-title":"Gender, heteroglossia and power: a sociolinguistic study of youth culture","author":"J Pujolar","year":"2001","unstructured":"Pujolar J (2001) Gender, heteroglossia and power: a sociolinguistic study of youth culture, vol 4. Walter de Gruyter, Berlin"},{"issue":"4","key":"1019_CR11","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1207\/S15327973RLSI3404_02","volume":"34","author":"J Cromdal","year":"2001","unstructured":"Cromdal J (2001) Overlap in bilingual play: some implications of code-switching for overlap resolution. Res Lang Soc Interact 34(4):421\u2013451","journal-title":"Res Lang Soc Interact"},{"key":"1019_CR12","doi-asserted-by":"publisher","DOI":"10.4324\/9780203017883","volume-title":"Code-switching in conversation: language, interaction and identity","author":"P Auer","year":"2013","unstructured":"Auer P (2013) Code-switching in conversation: language, interaction and identity. Routledge, Abingdon"},{"issue":"4","key":"1019_CR13","first-page":"651","volume":"22","author":"N Musk","year":"2012","unstructured":"Musk N (2012) \u201cPerforming bilingualism in wales,\u201d Pragmatics. Q Publ IPrA 22(4):651\u2013669","journal-title":"Q Publ IPrA"},{"key":"1019_CR14","first-page":"37","volume":"3","author":"A Vicentini","year":"2003","unstructured":"Vicentini A (2003) \u201cThe economy principle in language,\u201d Notes and Observations from early modern english grammars. Mots Palabras Words 3:37\u201357","journal-title":"Mots Palabras Words"},{"issue":"2","key":"1019_CR15","first-page":"179","volume":"126","author":"MH Bond","year":"1986","unstructured":"Bond MH, Lai T-M (1986) Embarrassment and code-switching into a second language. J Soc Psychol 126(2):179\u2013186","journal-title":"J Soc Psychol"},{"issue":"5","key":"1019_CR16","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1111\/1467-8721.00140","volume":"10","author":"RR Heredia","year":"2001","unstructured":"Heredia RR, Altarriba J (2001) Bilingual language mixing: why do bilinguals code-switch? Curr Direct Psychol Sci 10(5):164\u2013168","journal-title":"Curr Direct Psychol Sci"},{"issue":"1","key":"1019_CR17","first-page":"139","volume":"4","author":"JM Wei","year":"2003","unstructured":"Wei JM (2003) Codeswitching in campaigning discourse: the case of taiwanese president chen shui-bian. Lang Linguist 4(1):139\u2013165","journal-title":"Lang Linguist"},{"key":"1019_CR18","volume-title":"What the F: what swearing reveals about our language, our brains, and ourselves","author":"B Bergen","year":"2016","unstructured":"Bergen B (2016) What the F: what swearing reveals about our language, our brains, and ourselves. Basic Books, New York"},{"key":"1019_CR19","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139028462","volume-title":"A reference grammar of Chinese","author":"C-R Huang","year":"2016","unstructured":"Huang C-R, Shi D (2016) A reference grammar of Chinese. Cambridge University Press, Cambridge"},{"key":"1019_CR20","doi-asserted-by":"crossref","unstructured":"Lee S, Wang Z (2015) Emotion in code-switching texts: corpus construction and analysis. In: Proceedings of the Eighth SIGHAN workshop on chinese language processing, pp 91\u201399","DOI":"10.18653\/v1\/W15-3116"},{"issue":"3","key":"1019_CR21","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1109\/TASLP.2016.2637280","volume":"25","author":"Z Wang","year":"2017","unstructured":"Wang Z, Lee SYM, Li S, Zhou G (2017) Emotion analysis in code-switching text with joint factor graph model. IEEE\/ACM Trans Audio Speech Lang Process 25(3):469\u2013480","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"1019_CR22","unstructured":"Adel H, Vu NT, Schultz T (2013) Combination of recurrent neural networks and factored language models for code-switching language modeling. In: Proceedings of the 51st annual meeting of the association for computational linguistics (Volume 2: Short Papers), vol 2, pp 206\u2013211"},{"key":"1019_CR23","doi-asserted-by":"crossref","unstructured":"Solorio T, Liu Y (2008) Learning to predict code-switching points. In: Proceedings of the conference on empirical methods in natural language processing, Association for Computational Linguistics, pp 973\u2013981","DOI":"10.3115\/1613715.1613841"},{"key":"1019_CR24","unstructured":"Lignos C, Marcus M (2013) Toward web-scale analysis of codeswitching. In: 87th Annual Meeting of the Linguistic Society of America"},{"key":"1019_CR25","first-page":"1671","volume":"2012","author":"Y Li","year":"2012","unstructured":"Li Y, Fung P (2012) Code-switch language model with inversion constraints for mixed language speech recognition. Proc COLING 2012:1671\u20131680","journal-title":"Proc COLING"},{"key":"1019_CR26","unstructured":"Jamatia A, Gamb\u00e4ck B, Das A (2015) Part-of-speech tagging for code-mixed english-hindi twitter and facebook chat messages. In: Proceedings of the international conference recent advances in natural language processing, pp 239\u2013248"},{"key":"1019_CR27","doi-asserted-by":"crossref","unstructured":"Li S, Huang L, Wang R, Zhou G (2015) Sentence-level emotion classification with label and context dependence. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (Volume 1: Long Papers), vol 1, pp 1045\u20131053","DOI":"10.3115\/v1\/P15-1101"},{"key":"1019_CR28","unstructured":"Turney PD (2002) Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on association for computational linguistics, Association for Computational Linguistics, pp 417\u2013424"},{"key":"1019_CR29","unstructured":"Hatzivassiloglou V, McKeown KR (1997) Predicting the semantic orientation of adjectives. In: Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics, Association for Computational Linguistics, pp 174\u2013181"},{"key":"1019_CR30","unstructured":"McKeown K, Jordan D, Hatzivassiloglou V (1998) Generating patient-specific summaries of online literature. In: Proc. of Intelligent Text Summarization, AAAI Spring Symposium, Citeseer"},{"key":"1019_CR31","doi-asserted-by":"crossref","unstructured":"Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, Association for Computational Linguistics, pp 79\u201386","DOI":"10.3115\/1118693.1118704"},{"issue":"6","key":"1019_CR32","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1016\/j.ins.2010.11.023","volume":"181","author":"R Xia","year":"2011","unstructured":"Xia R, Zong C, Li S (2011) Ensemble of feature sets and classification algorithms for sentiment classification. Inf Sci 181(6):1138\u20131152","journal-title":"Inf Sci"},{"key":"1019_CR33","unstructured":"Socher R, Pennington J, Huang EH, Ng AY, Manning CD (2011) Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of the conference on empirical methods in natural language processing, Association for Computational Linguistics, pp 151\u2013161"},{"key":"1019_CR34","unstructured":"Socher R, Perelygin A, Wu JY, Chuang J, Manning CD, Ng AY, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), vol.\u00a01631, Citeseer, p 1642"},{"key":"1019_CR35","doi-asserted-by":"crossref","unstructured":"Irsoy O, Cardie C (2014) Opinion mining with deep recurrent neural networks. In: EMNLP, pp 720\u2013728","DOI":"10.3115\/v1\/D14-1080"},{"issue":"2","key":"1019_CR36","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157\u2013166","journal-title":"IEEE Trans Neural Netw"},{"issue":"8","key":"1019_CR37","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"1019_CR38","doi-asserted-by":"crossref","unstructured":"Tang D, Qin B, Liu T (2015) Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 conference on empirical methods in natural language processing (2015), pp 1422\u20131432","DOI":"10.18653\/v1\/D15-1167"},{"key":"1019_CR39","doi-asserted-by":"crossref","unstructured":"Tang D, Qin B, Liu T (2015) Learning semantic representations of users and products for document level sentiment classification. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (Volume 1: Long Papers), (Beijing, China), Association for Computational Linguistics, July, pp 1014\u20131023","DOI":"10.3115\/v1\/P15-1098"},{"key":"1019_CR40","doi-asserted-by":"crossref","unstructured":"Long Y, Ma M, Lu Q, Xiang R, Huang C.-R (2018) Dual memory network model for biased product review classification. arXiv preprint \n                    arXiv:1809.05807","DOI":"10.18653\/v1\/W18-6220"},{"key":"1019_CR41","doi-asserted-by":"crossref","unstructured":"Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 1480\u20131489","DOI":"10.18653\/v1\/N16-1174"},{"key":"1019_CR42","doi-asserted-by":"crossref","unstructured":"Long Y, Qin L, Xiang R, Li M, Huang C.-R (2017) A cognition based attention model for sentiment analysis. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 473\u2013482","DOI":"10.18653\/v1\/D17-1048"},{"key":"1019_CR43","doi-asserted-by":"crossref","unstructured":"Long Y, Xiang R, Lu Q, Huang C-R, Li M (2019) Improving attention model based on cognition grounded data for sentiment analysis. In: IEEE transactions on affective computing","DOI":"10.1109\/TAFFC.2019.2903056"},{"key":"1019_CR44","unstructured":"Wang Z, Zhang Y, Lee S, Li S, Zhou G (2016) A bilingual attention network for code-switched emotion prediction. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers, pp 1624\u20131634"},{"key":"1019_CR45","unstructured":"Keller MHF (2016) Modeling human reading with neural attention. In: Proceedings of the conference on empirical methods in natural language processing, p 95"},{"key":"1019_CR46","doi-asserted-by":"crossref","unstructured":"Wang J, Yu L.-C, Lai K.\u00a0R, Zhang X (2016) Dimensional sentiment analysis using a regional cnn-lstm model. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 2: Short Papers), vol 2, pp 225\u2013230","DOI":"10.18653\/v1\/P16-2037"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-019-01019-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-019-01019-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-019-01019-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T23:35:39Z","timestamp":1602200139000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-019-01019-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,10]]},"references-count":46,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2019,11]]}},"alternative-id":["1019"],"URL":"https:\/\/doi.org\/10.1007\/s13042-019-01019-z","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2019,10,10]]},"assertion":[{"value":"14 February 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}