{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:42:19Z","timestamp":1778168539123,"version":"3.51.4"},"reference-count":66,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T00:00:00Z","timestamp":1585699200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T00:00:00Z","timestamp":1585699200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1711262"],"award-info":[{"award-number":["U1711262"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61472453"],"award-info":[{"award-number":["61472453"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1501252"],"award-info":[{"award-number":["U1501252"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1711261"],"award-info":[{"award-number":["U1711261"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1611264"],"award-info":[{"award-number":["U1611264"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1401256"],"award-info":[{"award-number":["U1401256"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009329","name":"Scientific Research and Technology Development Program of Guangxi","doi-asserted-by":"publisher","award":["15248003-8"],"award-info":[{"award-number":["15248003-8"]}],"id":[{"id":"10.13039\/501100009329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012325","name":"National Office for Philosophy and Social Sciences","doi-asserted-by":"publisher","award":["2016YFB1000905"],"award-info":[{"award-number":["2016YFB1000905"]}],"id":[{"id":"10.13039\/501100012325","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012325","name":"National Office for Philosophy and Social Sciences","doi-asserted-by":"publisher","award":["ZD186"],"award-info":[{"award-number":["ZD186"]}],"id":[{"id":"10.13039\/501100012325","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012325","name":"National Office for Philosophy and Social Sciences","doi-asserted-by":"publisher","award":["13"],"award-info":[{"award-number":["13"]}],"id":[{"id":"10.13039\/501100012325","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition Letters"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1016\/j.patrec.2018.06.027","type":"journal-article","created":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T22:28:39Z","timestamp":1530138519000},"page":"115-122","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":39,"special_numbering":"C","title":["Multi-task learning using variational auto-encoder for sentiment classification"],"prefix":"10.1016","volume":"132","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6908-6269","authenticated-orcid":false,"given":"Guangquan","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xishun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.patrec.2018.06.027_bib0001","series-title":"Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"1005","article-title":"Multitask learning for fine-grained twitter sentiment analysis","author":"Balikas","year":"2017"},{"key":"10.1016\/j.patrec.2018.06.027_bib0002","series-title":"International Conference on Acoustics, Speech, and Signal Processing","first-page":"8624","article-title":"Advances in optimizing recurrent networks","author":"Bengio","year":"2013"},{"issue":"8","key":"10.1016\/j.patrec.2018.06.027_bib0003","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: a review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"10.1016\/j.patrec.2018.06.027_bib0004","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.patrec.2018.06.027_bib0005","series-title":"International Conference on Machine Learning","first-page":"1159","article-title":"Modeling temporal dependencies in high-dimensional sequences: application to polyphonic music generation and transcription","author":"Boulangerlewandowski","year":"2012"},{"key":"10.1016\/j.patrec.2018.06.027_bib0006","series-title":"Conference on Computational Natural Language Learning","first-page":"10","article-title":"Generating sentences from a continuous space","author":"Bowman","year":"2016"},{"issue":"1","key":"10.1016\/j.patrec.2018.06.027_bib0007","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","article-title":"Multitask learning","volume":"28","author":"Caruana","year":"1997","journal-title":"Mach. Learn."},{"key":"10.1016\/j.patrec.2018.06.027_bib0008","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.eswa.2016.10.065","article-title":"Improving sentiment analysis via sentence type classification using biLSTM-CRF and CNN","volume":"72","author":"Chen","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.patrec.2018.06.027_bib0009","first-page":"1724","article-title":"Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation","author":"Cho","year":"2014","journal-title":"empirical methods in natural language processing"},{"key":"10.1016\/j.patrec.2018.06.027_bib0010","unstructured":"F. Chollet, et\u00a0al., Keras, 2015, (https:\/\/github.com\/fchollet\/keras)."},{"key":"10.1016\/j.patrec.2018.06.027_bib0011","series-title":"Neural Information Processing Systems","first-page":"2980","article-title":"A recurrent latent variable model for sequential data","author":"Chung","year":"2015"},{"key":"10.1016\/j.patrec.2018.06.027_bib0012","series-title":"International Conference on Machine Learning","first-page":"160","article-title":"A unified architecture for natural language processing: deep neural networks with multitask learning","author":"Collobert","year":"2008"},{"key":"10.1016\/j.patrec.2018.06.027_bib0013","doi-asserted-by":"crossref","unstructured":"A. Conneau, H. Schwenk, L. Barrault, Y. Lecun, Very deep convolutional networks for natural language processing, arXiv: Computation and Language (2016).","DOI":"10.18653\/v1\/E17-1104"},{"key":"10.1016\/j.patrec.2018.06.027_bib0014","series-title":"Conference of the International Speech Communication Association","first-page":"1692","article-title":"Binary coding of speech spectrograms using a deep auto-encoder","author":"Deng","year":"2010"},{"key":"10.1016\/j.patrec.2018.06.027_bib0015","unstructured":"C. Doersch, Tutorial on variational autoencoders, arXiv: Machine Learning (2016)."},{"key":"10.1016\/j.patrec.2018.06.027_bib0016","series-title":"Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks","first-page":"189","article-title":"Recurrent nets that time and count","volume":"3","author":"Gers","year":"2000"},{"key":"10.1016\/j.patrec.2018.06.027_sbref0014","series-title":"Deep learning","author":"Goodfellow","year":"2016"},{"key":"10.1016\/j.patrec.2018.06.027_bib0018","article-title":"Generating sequences with recurrent neural networks","author":"Graves","year":"2013","journal-title":"Neural Evol. Comput."},{"issue":"5","key":"10.1016\/j.patrec.2018.06.027_bib0019","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1109\/TPAMI.2008.137","article-title":"A novel connectionist system for unconstrained handwriting recognition","volume":"31","author":"Graves","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patrec.2018.06.027_bib0020","series-title":"International Conference on Acoustics, Speech, and Signal Processing","first-page":"6645","article-title":"Speech recognition with deep recurrent neural networks","author":"Graves","year":"2013"},{"key":"10.1016\/j.patrec.2018.06.027_bib0021","series-title":"International Conference on Machine Learning","first-page":"1462","article-title":"Draw: a recurrent neural network for image generation","author":"Gregor","year":"2015"},{"key":"10.1016\/j.patrec.2018.06.027_sbref0019","article-title":"Unsupervised feature selection with graph learning via low-rank constraint","author":"Guangquan","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.patrec.2018.06.027_sbref0020","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.knosys.2017.02.030","article-title":"Learning representations from heterogeneous network for sentiment classification of product reviews","volume":"124","author":"Gui","year":"2017","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.patrec.2018.06.027_bib0024","series-title":"Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2015"},{"key":"10.1016\/j.patrec.2018.06.027_bib0025","series-title":"European Conference on Computer Vision","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"He","year":"2016"},{"key":"10.1016\/j.patrec.2018.06.027_bib0026","series-title":"International World Wide Web Conferences","article-title":"Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering","author":"He","year":"2016"},{"issue":"8","key":"10.1016\/j.patrec.2018.06.027_bib0027","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.patrec.2018.06.027_bib0028","series-title":"Empirical Methods in Natural Language Processing","first-page":"1746","article-title":"Convolutional neural networks for sentence classification","author":"Kim","year":"2014"},{"key":"10.1016\/j.patrec.2018.06.027_bib0029","series-title":"International Conference on Learning Representations","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2015"},{"key":"10.1016\/j.patrec.2018.06.027_bib0030","series-title":"Neural Information Processing systems","first-page":"3581","article-title":"Semi-supervised learning with deep generative models","volume":"27","author":"Kingma","year":"2014"},{"key":"10.1016\/j.patrec.2018.06.027_bib0031","series-title":"International Conference on Learning Representations","article-title":"Auto-encoding variational Bayes","author":"Kingma","year":"2014"},{"key":"10.1016\/j.patrec.2018.06.027_bib0032","series-title":"International Conference on Machine Learning","first-page":"1863","article-title":"A clockwork RNN","author":"Koutnik","year":"2014"},{"key":"10.1016\/j.patrec.2018.06.027_bib0033","series-title":"International Conference on Machine Learning","first-page":"1188","article-title":"Distributed representations of sentences and documents","author":"Le","year":"2014"},{"key":"10.1016\/j.patrec.2018.06.027_bib0034","article-title":"Unsupervised feature selection via local structure learning and sparse learning","author":"Lei","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.patrec.2018.06.027_bib0035","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.procs.2014.05.296","article-title":"Sentiment classification based on AS-LDA model","volume":"31","author":"Liang","year":"2014","journal-title":"Procedia - Procedia Comput. Sci."},{"key":"10.1016\/j.patrec.2018.06.027_bib0036","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.neucom.2016.01.110","article-title":"Neurocomputing topic-related chinese message sentiment analysis","volume":"210","author":"Liao","year":"2016","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.patrec.2018.06.027_bib0037","first-page":"1","article-title":"Sentiment analysis and opinion mining","volume":"5","author":"Liu","year":"2012","journal-title":"Synth. Lect. Human Lang. Technol."},{"key":"10.1016\/j.patrec.2018.06.027_bib0038","series-title":"Conference on Empirical Methods in Natural Language Processing","article-title":"Deep multi-task learning with shared memory","author":"Liu","year":"2016"},{"key":"10.1016\/j.patrec.2018.06.027_bib0039","series-title":"International Joint Conference on Artificial Intelligence","first-page":"2873","article-title":"Recurrent neural network for text classification with multi-task learning","author":"Liu","year":"2016"},{"key":"10.1016\/j.patrec.2018.06.027_bib0040","doi-asserted-by":"crossref","unstructured":"X. Liu, J. Gao, X. He, L. Deng, K. Duh, Y. Wang, Representation learning using multi-task deep neural networks for semantic classification and information retrieval(2015) 912\u2013921.","DOI":"10.3115\/v1\/N15-1092"},{"key":"10.1016\/j.patrec.2018.06.027_bib0041","series-title":"International Conference on Machine Learning","first-page":"1445","article-title":"Auxiliary deep generative models","author":"Maaloe","year":"2016"},{"key":"10.1016\/j.patrec.2018.06.027_bib0042","series-title":"Interactions between Data Mining and Natural Language Processing","article-title":"Correcting linguistic training bias in an faqbot using lstm-vae","author":"Patidar","year":"2017"},{"key":"10.1016\/j.patrec.2018.06.027_bib0043","series-title":"International Conference on Machine Learning","first-page":"1727","article-title":"Neural variational inference for text processing","author":"Miao","year":"2016"},{"key":"10.1016\/j.patrec.2018.06.027_bib0044","unstructured":"T. Mikolov, K. Chen, G. Corrado, J. Dean, Efficient estimation of word representations in vector space, arXiv: Computation and Language (2013a)."},{"key":"10.1016\/j.patrec.2018.06.027_bib0045","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","author":"Mikolov","year":"2013","journal-title":"neural information processing systems"},{"key":"10.1016\/j.patrec.2018.06.027_bib0046","series-title":"Proceedings of the IEEE First International Conference on Neural Networks (San Diego, CA)","first-page":"11","article-title":"Kolmogorov\u2019s mapping neural network existence theorem","volume":"III","author":"Nielsen","year":"1987"},{"key":"10.1016\/j.patrec.2018.06.027_bib0047","series-title":"Empirical Methods in Natural Language Processing","first-page":"79","article-title":"Thumbs up? Sentiment classification using machine learning techniques","author":"Pang","year":"2002"},{"key":"10.1016\/j.patrec.2018.06.027_bib0048","first-page":"2825","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.patrec.2018.06.027_bib0049","series-title":"Empirical Methods in Natural Language Processing (EMNLP)","first-page":"1532","article-title":"Glove: Global vectors for word representation","author":"Pennington","year":"2014"},{"key":"10.1016\/j.patrec.2018.06.027_bib0050","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.knosys.2016.06.009","article-title":"Aspect extraction for opinion mining with a deep convolutional neural network","volume":"108","author":"Poria","year":"2016","journal-title":"Knowl. Based Syst."},{"issue":"6088","key":"10.1016\/j.patrec.2018.06.027_bib0051","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"issue":"4","key":"10.1016\/j.patrec.2018.06.027_bib0052","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1080\/09540098908915650","article-title":"A local learning algorithm for dynamic feed forward and recurrent networks","volume":"1","author":"Schmidhuber","year":"1989","journal-title":"Conn. Sci."},{"key":"10.1016\/j.patrec.2018.06.027_bib0053","series-title":"Gradient flow in recurrent nets: the difficulty of learning longterm dependencies","author":"Sepp Hochreiter","year":"2001"},{"key":"10.1016\/j.patrec.2018.06.027_bib0054","series-title":"Thirty-First AAAI Conference on Artificial Intelligence","article-title":"Variational autoencoder for semi-supervised text classification","author":"Xu","year":"2017"},{"key":"10.1016\/j.patrec.2018.06.027_bib0055","series-title":"Empirical Methods in Natural Language Processing","first-page":"1711","article-title":"Semantically conditioned LSTM-based natural language generation for spoken dialogue systems","author":"Wen","year":"2015"},{"issue":"4","key":"10.1016\/j.patrec.2018.06.027_bib0056","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1109\/TASL.2010.2064309","article-title":"A probabilistic interaction model for multipitch tracking with factorial hidden Markov models","volume":"19","author":"Wohlmayr","year":"2011","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"10.1016\/j.patrec.2018.06.027_bib0057","series-title":"International Conference on Machine Learning","article-title":"Improved variational autoencoders for text modeling using dilated convolutions","author":"Yang","year":"2017"},{"key":"10.1016\/j.patrec.2018.06.027_bib0058","article-title":"Depth-gated lstm","author":"Yao","year":"2015","journal-title":"Neural Evol. Comput."},{"key":"10.1016\/j.patrec.2018.06.027_bib0059","series-title":"Workshop on Applications of Computer Vision","first-page":"1036","article-title":"Improving multiview face detection with multi-task deep convolutional neural networks","author":"Zhang","year":"2014"},{"issue":"5","key":"10.1016\/j.patrec.2018.06.027_bib0060","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1109\/TNNLS.2017.2673241","article-title":"Efficient KNN classification with different numbers of nearest neighbors","volume":"29","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.patrec.2018.06.027_bib0061","first-page":"649","article-title":"Character-level convolutional networks for text classification","author":"Zhang","year":"2015","journal-title":"Neural Inf. Process. Syst."},{"key":"10.1016\/j.patrec.2018.06.027_bib0062","series-title":"European Conference on Computer Vision","first-page":"94","article-title":"Facial landmark detection by deep multi-task learning","author":"Zhang","year":"2014"},{"key":"10.1016\/j.patrec.2018.06.027_bib0063","article-title":"Dynamic graph learning for spectral feature selection","author":"Zheng","year":"2017","journal-title":"Multimed. Tools Appl."},{"issue":"9","key":"10.1016\/j.patrec.2018.06.027_bib0064","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1109\/TMM.2017.2703636","article-title":"Graph pca hashing for similarity search","volume":"19","author":"Zhu","year":"2017","journal-title":"IEEE Trans. Multimedia"},{"issue":"3","key":"10.1016\/j.patrec.2018.06.027_bib0065","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1109\/TKDE.2017.2763618","article-title":"Local and global structure preservation for robust unsupervised spectral feature selection","volume":"30","author":"Zhu","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.patrec.2018.06.027_bib0066","series-title":"IPMI","first-page":"264","article-title":"Early diagnosis of alzheimer\u2019s disease by joint feature selection and classification on temporally structured support vector machine","author":"Zhu","year":"2016"}],"container-title":["Pattern Recognition Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167865518302769?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167865518302769?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T06:09:17Z","timestamp":1759126157000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167865518302769"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4]]},"references-count":66,"alternative-id":["S0167865518302769"],"URL":"https:\/\/doi.org\/10.1016\/j.patrec.2018.06.027","relation":{},"ISSN":["0167-8655"],"issn-type":[{"value":"0167-8655","type":"print"}],"subject":[],"published":{"date-parts":[[2020,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-task learning using variational auto-encoder for sentiment classification","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition Letters","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patrec.2018.06.027","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2018 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}