{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T17:26:44Z","timestamp":1762018004797,"version":"3.41.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2017,3,28]],"date-time":"2017-03-28T00:00:00Z","timestamp":1490659200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2017,3,28]],"date-time":"2017-03-28T00:00:00Z","timestamp":1490659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NS-1427536"],"award-info":[{"award-number":["NS-1427536"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"published-print":{"date-parts":[[2017,12]]},"DOI":"10.1186\/s40537-017-0065-8","type":"journal-article","created":{"date-parts":[[2017,3,28]],"date-time":"2017-03-28T01:22:28Z","timestamp":1490664148000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Improving deep neural network design with new text data representations"],"prefix":"10.1186","volume":"4","author":[{"given":"Joseph D.","family":"Prusa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taghi M.","family":"Khoshgoftaar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,3,28]]},"reference":[{"key":"65_CR1","unstructured":"Zhang X, LeCun Y. Text understanding from scratch. 2015. arXiv preprint arXiv:1502.01710 ."},{"key":"65_CR2","unstructured":"Prusa JD, Khoshgoftaar TM, Dittman DJ. Impact of feature selection techniques for tweet sentiment classification. In: Proceedings of the 28th International FLAIRS Conference; 2015. p. 299\u2013304."},{"key":"65_CR3","doi-asserted-by":"crossref","unstructured":"Saif H, He Y, Alani H. Semantic sentiment analysis of twitter. In: The semantic web\u2013ISWC. Berlin: Springer; 2012. p. 508\u201324.","DOI":"10.1007\/978-3-642-35176-1_32"},{"key":"65_CR4","first-page":"538","volume":"11","author":"E Kouloumpis","year":"2011","unstructured":"Kouloumpis E, Wilson T, Moore J. Twitter sentiment analysis: the good the bad and the omg!. ICWSM. 2011;11:538\u201341.","journal-title":"ICWSM"},{"key":"65_CR5","unstructured":"Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R. Sentiment analysis of twitter data. In: Proceedings of the workshop on languages in social media. Association for Computational Linguistics; 2011. p. 30\u20138."},{"key":"65_CR6","unstructured":"Trim C. NLP-driven ontology modeling. 2012. http:\/\/www.ibm.com\/developerworks\/community\/blogs\/nlp\/entry\/nlp_driven_ontology_modeling8?lang=en ."},{"key":"65_CR7","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Info Proc Syst. 2012: 1097\u2013105."},{"issue":"1","key":"65_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-014-0007-7","volume":"2","author":"MM Najafabadi","year":"2015","unstructured":"Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E. Deep learning applications and challenges in big data analytics. J Big Data. 2015;2(1):1\u201321.","journal-title":"J Big Data"},{"key":"65_CR9","unstructured":"Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. 2013. arXiv preprint arXiv:1301.3781 ."},{"key":"65_CR10","doi-asserted-by":"crossref","unstructured":"Tang D, Qin B, Liu T. Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 conference on empirical methods in natural language processing; 2015. p. 1422\u2013432.","DOI":"10.18653\/v1\/D15-1167"},{"key":"65_CR11","doi-asserted-by":"crossref","unstructured":"Graves A. Neural networks. In: Supervised sequence labelling with recurrent neural networks. Berlin: Springer. p. 15\u201335.","DOI":"10.1007\/978-3-642-24797-2_3"},{"key":"65_CR12","unstructured":"Chetlur S, Woolley C, Vandermersch P, Cohen J, Tran J, Catanzaro B, Shelhamer E. cudnn: efficient primitives for deep learning. 2014. arXiv preprint arXiv:1410.0759 ."},{"key":"65_CR13","doi-asserted-by":"crossref","unstructured":"Collobert R, Weston J. A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning. New York city: ACM; 2008. p. 160\u201367.","DOI":"10.1145\/1390156.1390177"},{"key":"65_CR14","doi-asserted-by":"crossref","unstructured":"Kim Y. Convolutional neural networks for sentence classification. 2014. arXiv preprint arXiv:1408.5882 .","DOI":"10.3115\/v1\/D14-1181"},{"key":"65_CR15","doi-asserted-by":"crossref","unstructured":"Poria S, Cambria E, Gelbukh A. Deep convolutional neural network textual features and multiple kernel learning for utterance-level multimodal sentiment analysis. In: Proceedings of EMNLP; 2015. p. 2539\u2013544.","DOI":"10.18653\/v1\/D15-1303"},{"key":"65_CR16","unstructured":"Nair V, Hinton GE. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning: ICML-10; 2010. p. 807\u201314."},{"key":"65_CR17","unstructured":"Boureau YL, Ponce J, LeCun Y. A theoretical analysis of feature pooling in visual recognition. In: Proceedings of the 27th international conference on machine learning: ICML-10; 2010. p. 111\u201318."},{"issue":"1","key":"65_CR18","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15(1):1929\u201358.","journal-title":"J Mach Learn Res"},{"key":"65_CR19","doi-asserted-by":"crossref","unstructured":"Bridle JS. Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In: Neurocomputing. Berlin: Springer; 1990. p. 227\u201336.","DOI":"10.1007\/978-3-642-76153-9_28"},{"key":"65_CR20","doi-asserted-by":"crossref","unstructured":"Goodman, J.: Classes for fast maximum entropy training. In: Proceedings IEEE international conference on acoustics, speech, and signal processing, vol. 1. Piscataway: IEEE. p. 561\u201364.","DOI":"10.1109\/ICASSP.2001.940893"},{"key":"65_CR21","unstructured":"Go A, Bhayani R, Huang L. Twitter sentiment classification using distant supervision. CS224N Project Report: Stanford; 2009. p. 1\u201312."},{"key":"65_CR22","unstructured":"Bastien F, Lamblin P, Pascanu R, Bergstra J, Goodfellow IJ, Bergeron A, Bouchard N, Bengio Y. Theano: new features and speed improvements. Deep learning and unsupervised feature learning NIPS 2012 Workshop. 2012."},{"key":"65_CR23","doi-asserted-by":"crossref","unstructured":"Bergstra J, Breuleux O, Bastien F, Lamblin P, Pascanu R, Desjardins G, Turian J, Warde-Farley D, Bengio Y. Theano: a CPU and GPU math expression compiler. In: Proceedings of the Python for scientific computing conference: SciPy; 2010. Oral Presentation.","DOI":"10.25080\/Majora-92bf1922-003"},{"issue":"2","key":"65_CR24","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1145\/1365490.1365500","volume":"6","author":"J Nickolls","year":"2008","unstructured":"Nickolls J, Buck I, Garland M, Skadron K. Scalable parallel programming with cuda. Queue. 2008;6(2):40\u201353.","journal-title":"Queue"},{"key":"65_CR25","doi-asserted-by":"publisher","unstructured":"Dieleman S, Schl\u00fcter J, Raffel C, Olson E, S\u00f8nderby SK, Nouri D, Maturana D, Thoma M, Battenberg E, Kelly J, Fauw JD, Heilman M, diogo149, McFee B, Weideman H, takacsg84, peterderivaz, Jon, instagibbs, Rasul DK, CongLiu, Britefury, Degrave J. Lasagne: first release. 2015. doi: 10.5281\/zenodo.27878 .","DOI":"10.5281\/zenodo.27878"},{"key":"65_CR26","volume-title":"Intermediate statistical methods and applications: a computer package approach","author":"ML Berenson","year":"1983","unstructured":"Berenson ML, Goldstein M, Levine D. Intermediate statistical methods and applications: a computer package approach. 2nd ed. Upper Saddle River: Prentice Hall; 1983.","edition":"2"},{"key":"65_CR27","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE Conference on computer vision and pattern recognition. 2015. p. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-017-0065-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s40537-017-0065-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-017-0065-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:16:00Z","timestamp":1750184160000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-017-0065-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,28]]},"references-count":27,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,12]]}},"alternative-id":["65"],"URL":"https:\/\/doi.org\/10.1186\/s40537-017-0065-8","relation":{},"ISSN":["2196-1115"],"issn-type":[{"type":"electronic","value":"2196-1115"}],"subject":[],"published":{"date-parts":[[2017,3,28]]},"assertion":[{"value":"4 November 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2017","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"7"}}