{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T16:10:24Z","timestamp":1786983024590,"version":"3.56.0"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,2,8]],"date-time":"2019-02-08T00:00:00Z","timestamp":1549584000000},"content-version":"vor","delay-in-days":38,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001700","name":"Ministry of Education, Culture, Sports, Science and Technology","doi-asserted-by":"publisher","award":["Exploratory Challenges on Post-K computer"],"award-info":[{"award-number":["Exploratory Challenges on Post-K computer"]}],"id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["17H02041"],"award-info":[{"award-number":["17H02041"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Soc Sc"],"published-print":{"date-parts":[[2019,1]]},"DOI":"10.1007\/s42001-019-00035-x","type":"journal-article","created":{"date-parts":[[2019,2,7]],"date-time":"2019-02-07T23:37:05Z","timestamp":1549582625000},"page":"33-46","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":118,"title":["Enhanced news sentiment analysis using deep learning methods"],"prefix":"10.1007","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1051-4350","authenticated-orcid":false,"given":"Wataru","family":"Souma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1183-7941","authenticated-orcid":false,"given":"Irena","family":"Vodenska","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2239-4330","authenticated-orcid":false,"given":"Hideaki","family":"Aoyama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,2,8]]},"reference":[{"issue":"7","key":"35_CR1","doi-asserted-by":"publisher","first-page":"e0180944","DOI":"10.1371\/journal.pone.0180944","volume":"12","author":"W Bao","year":"2017","unstructured":"Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one, 12(7), e0180944.","journal-title":"PloS one"},{"key":"35_CR2","unstructured":"Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2016). Enriching word vectors with subword information. CoRR (abs\/1607.04606) \n                    arXiv:abs\/1607.04606"},{"key":"35_CR3","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.eswa.2017.04.030","volume":"83","author":"E Chong","year":"2017","unstructured":"Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies. Expert Systems with Applications, 83, 187\u2013205.","journal-title":"Expert Systems with Applications"},{"key":"35_CR4","unstructured":"Gigaword5: \n                    https:\/\/catalog.ldc.upenn.edu\/LDC2011T07"},{"key":"35_CR5","unstructured":"GloVe: \n                    https:\/\/nlp.stanford.edu\/projects\/glove\/"},{"key":"35_CR6","first-page":"20","volume":"2018","author":"SG H\u00e4ndschke","year":"2018","unstructured":"H\u00e4ndschke, S. G., Buechel, S., Goldenstein, J., Poschmann, P., Duan, T., Walgenbach, P., et al. (2018). A corpus of corporate annual and social responsibility reports: 280\u00a0million tokens of balanced organizational writing. ACL, 2018, 20.","journal-title":"ACL"},{"issue":"1","key":"35_CR7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/asmb.2209","volume":"33","author":"J Heaton","year":"2017","unstructured":"Heaton, J., Polson, N., & Witte, J. H. (2017). Deep learning for finance: deep portfolios. Applied Stochastic Models in Business and Industry, 33(1), 3\u201312.","journal-title":"Applied Stochastic Models in Business and Industry"},{"issue":"8","key":"35_CR8","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 computation, 9(8), 1735\u20131780.","journal-title":"Neural computation"},{"issue":"3","key":"35_CR9","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1162\/089120103322711569","volume":"29","author":"A Kilgarriff","year":"2003","unstructured":"Kilgarriff, A., & Grefenstette, G. (2003). Introduction to the special issue on the web as corpus. Computational linguistics, 29(3), 333\u2013347.","journal-title":"Computational linguistics"},{"key":"35_CR10","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint \n                    arXiv:1408.5882","DOI":"10.3115\/v1\/D14-1181"},{"key":"35_CR11","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.dss.2017.10.001","volume":"104","author":"M Kraus","year":"2017","unstructured":"Kraus, M., & Feuerriegel, S. (2017). Decision support from financial disclosures with deep neural networks and transfer learning. Decision Support Systems, 104, 38\u201348.","journal-title":"Decision Support Systems"},{"key":"35_CR12","unstructured":"Lee, S. I., & Yoo, S. J. (2017). A deep efficient frontier method for optimal investments. arXiv preprint \n                    arXiv:1709.09822"},{"key":"35_CR13","unstructured":"Lee, S. I., & Yoo, S. J. (2018). A new method for portfolio construction using a deep predictive model. In: Proceedings of the 7th International Conference on Emerging Databases (pp. 260\u2013266). Springer"},{"key":"35_CR14","unstructured":"Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. CoRR (abs\/1301.3781), \n                    arxiv:1301.3781"},{"key":"35_CR15","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In: C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Q. Weinberger (Eds.), Advances in neural information processing systems (vol. 26, pp. 3111\u20133119). Curran Associates, Inc. \n                    http:\/\/papers.nips.cc\/paper\/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf\n                    \n                  ."},{"issue":"11","key":"35_CR16","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller, G. A. (1995). Wordnet: a lexical database for english. Communications of the ACM, 38(11), 39.","journal-title":"Communications of the ACM"},{"key":"35_CR17","unstructured":"Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532\u20131543)."},{"key":"35_CR18","doi-asserted-by":"publisher","first-page":"9072948","DOI":"10.1155\/2018\/9072948","volume":"2018","author":"L Ponta","year":"2018","unstructured":"Ponta, L., & Cincotti, S. (2018). Traders networks of interactions and structural properties of financial markets: an agent-based approach. Complexity, 2018, 9072948. \n                    https:\/\/doi.org\/10.1155\/2018\/9072948\n                    \n                  .","journal-title":"Complexity"},{"issue":"1","key":"35_CR19","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s40537-017-0111-6","volume":"5","author":"S Sohangir","year":"2018","unstructured":"Sohangir, S., Wang, D., Pomeranets, A., & Khoshgoftaar, T. M. (2018). Big data: deep learning for financial sentiment analysis. Journal of Big Data, 5(1), 3. \n                    https:\/\/doi.org\/10.1186\/s40537-017-0111-6\n                    \n                  .","journal-title":"Journal of Big Data"},{"key":"35_CR20","unstructured":"Troiano, L., Mejuto, E., & Kriplani, P. (2017). On feature reduction using deep learning for trend prediction in finance. arXiv preprint \n                    arXiv:1704.03205"},{"key":"35_CR21","doi-asserted-by":"crossref","unstructured":"Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., & Iosifidis, A. (2017). Forecasting stock prices from the limit order book using convolutional neural networks. In: Business informatics (CBI), 2017 IEEE 19th conference on. vol. 1, pp. 7\u201312. IEEE","DOI":"10.1109\/CBI.2017.23"},{"key":"35_CR22","doi-asserted-by":"crossref","unstructured":"Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., & Iosifidis, A. (2017). Using deep learning to detect price change indications in financial markets. In: Signal processing conference (EUSIPCO), 2017 25th European. pp. 2511\u20132515. IEEE","DOI":"10.23919\/EUSIPCO.2017.8081663"},{"issue":"1","key":"35_CR23","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s10462-017-9588-9","volume":"50","author":"FZ Xing","year":"2018","unstructured":"Xing, F. Z., Cambria, E., & Welsch, R. E. (2018). Natural language based financial forecasting: a survey. Artificial Intelligence Review, 50(1), 49\u201373.","journal-title":"Artificial Intelligence Review"}],"container-title":["Journal of Computational Social Science"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s42001-019-00035-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s42001-019-00035-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s42001-019-00035-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,14]],"date-time":"2020-05-14T06:25:48Z","timestamp":1589437548000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s42001-019-00035-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1]]},"references-count":23,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,1]]}},"alternative-id":["35"],"URL":"https:\/\/doi.org\/10.1007\/s42001-019-00035-x","relation":{},"ISSN":["2432-2717","2432-2725"],"issn-type":[{"value":"2432-2717","type":"print"},{"value":"2432-2725","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1]]},"assertion":[{"value":"10 January 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 February 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}