{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T06:09:13Z","timestamp":1780726153067,"version":"3.54.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T00:00:00Z","timestamp":1628640000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T00:00:00Z","timestamp":1628640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pers Ubiquit Comput"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s00779-021-01595-4","type":"journal-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T06:03:03Z","timestamp":1628661783000},"page":"2055-2069","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Deep associative learning approach for bio-medical sentiment analysis utilizing unsupervised representation from large-scale patients\u2019 narratives"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0904-5868","authenticated-orcid":false,"given":"Hanane","family":"Grissette","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"El Habib","family":"Nfaoui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,11]]},"reference":[{"issue":"1","key":"1595_CR1","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1007\/s13721-020-00248-5","volume":"9","author":"H Grissette","year":"2020","unstructured":"Grissette H, Nfaoui EH (2020) Enhancing convolution-based sentiment extractor via dubbed N-gram embedding-related drug vocabulary. Netw Model Anal Health Inf Bioinform 9(1):42","journal-title":"Netw Model Anal Health Inf Bioinform"},{"key":"1595_CR2","unstructured":"Rodrigues RG, das Dores RM, Camilo-Junior CG, Rosa TC (2014) SentiHealth-Cancer: a sentiment analysis tool to help detecting mood of patients in online social networks. Int J Med Inform"},{"key":"1595_CR3","doi-asserted-by":"crossref","unstructured":"Giustini DM, Ali SM, Fraser M, Boulos MNK (2018) Effective uses of social media in public health and medicine: a systematic review of systematic reviews. Online Journal of Public Health Informatics","DOI":"10.5210\/ojphi.v10i2.8270"},{"key":"1595_CR4","doi-asserted-by":"crossref","unstructured":"Portier K, Greer GE, Rokach L, Ofek N, Wang Y, Biyani P, Yu M, Banerjee S, Zhao K, Mitra P, Yen J (2013) Understanding topics and sentiment in an online cancer survivor community. Journal of the National Cancer Institute - Monographs","DOI":"10.1093\/jncimonographs\/lgt025"},{"key":"1595_CR5","doi-asserted-by":"crossref","unstructured":"Grissette H, Nfaoui EH (2019) A conditional sentiment analysis model for the embedding patient self-report experiences on social media. Advances in Intelligent Systems and Computing","DOI":"10.1007\/978-3-030-11884-6_6"},{"key":"1595_CR6","doi-asserted-by":"crossref","unstructured":"Enquist M, Lind J, Ghirlanda S (2016) The power of associative learning and the ontogeny of optimal behaviour. Royal Society Open Science","DOI":"10.1098\/rsos.160734"},{"key":"1595_CR7","doi-asserted-by":"crossref","unstructured":"Nikfarjam A, Sarker A, O\u2019Connor K, Ginn R, Gonzalez G (2015) Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. J Am Med Inform Assoc","DOI":"10.1093\/jamia\/ocu041"},{"key":"1595_CR8","doi-asserted-by":"crossref","unstructured":"Hu H, Phan NH, Geller J, Vo H, Manasi B, Huang X, Di Lorio S, Dinh T, Chun SA (2018) Deep self-taught learning for detecting drug abuse risk behavior in tweets. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","DOI":"10.1007\/978-3-030-04648-4_28"},{"key":"1595_CR9","doi-asserted-by":"crossref","unstructured":"Araque O, Corcuera-Platas I, S\u00e1nchez-Rada J F, Iglesias CA (2017) Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Syst Appl","DOI":"10.1016\/j.eswa.2017.02.002"},{"key":"1595_CR10","doi-asserted-by":"crossref","unstructured":"Grisstte H, Nfaoui E (2019) Daily life patients sentiment analysis model based on well-encoded embedding vocabulary for related-medication text. In: Proceedings of the 2019 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM \u201919. Association for Computing Machinery, New York, pp 921\u2013928","DOI":"10.1145\/3341161.3343854"},{"key":"1595_CR11","doi-asserted-by":"crossref","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.1109\/TPAMI.2013.50"},{"key":"1595_CR12","unstructured":"Baccianella S, Esuli A, Sebastiani F (2010) SENTIWORDNET 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the 7th International Conference on Language Resources and Evaluation, LREC 2010"},{"key":"1595_CR13","doi-asserted-by":"publisher","first-page":"106423","DOI":"10.1016\/j.knosys.2020.106423","volume":"213","author":"O Mohamad Beigi","year":"2021","unstructured":"Mohamad Beigi O, Moattar MH (2021) Automatic construction of domain-specific sentiment lexicon for unsupervised domain adaptation and sentiment classification. Knowl-Based Syst 213:106423","journal-title":"Knowl-Based Syst"},{"key":"1595_CR14","doi-asserted-by":"publisher","first-page":"37075","DOI":"10.1109\/ACCESS.2021.3062654","volume":"9","author":"Y Wang","year":"2021","unstructured":"Wang Y, Huang G, Li J, Li H, Zhou Y, Jiang H (2021) Refined global word embeddings based on sentiment concept for sentiment analysis. IEEE Access 9:37075\u201337085","journal-title":"IEEE Access"},{"issue":"1","key":"1595_CR15","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1038\/s41597-019-0055-0","volume":"6","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Chen Q, Yang Z, Lin H, Lu Z (2019) BioWordVec, improving biomedical word embeddings with subword information and MeSH. Sci Data 6(1):52","journal-title":"Sci Data"},{"key":"1595_CR16","doi-asserted-by":"crossref","unstructured":"Chen Q, Peng Y, Lu Z (2018) BioSentVec: creating sentence embeddings for biomedical texts. arXiv:http:\/\/arxiv.org\/abs\/1810.09302","DOI":"10.1109\/ICHI.2019.8904728"},{"key":"1595_CR17","doi-asserted-by":"crossref","unstructured":"Chen Q, Lee K, Yan S, Kim S, Wei CH, Lu Z (2020) Bioconceptvec: creating and evaluating literature-based biomedical concept embeddings on a large scale. PLoS Comput Biol","DOI":"10.1371\/journal.pcbi.1007617"},{"key":"1595_CR18","unstructured":"PubTator: a web-based text mining tool for assisting biocuration"},{"key":"1595_CR19","doi-asserted-by":"crossref","unstructured":"Grissette H, Nfaoui EH (2020) Drug reaction discriminator within encoder-decoder neural network model: COVID-19 pandemic case study. In: 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS). IEEE","DOI":"10.1109\/SNAMS52053.2020.9336561"},{"key":"1595_CR20","doi-asserted-by":"crossref","unstructured":"Krotov D, Hopfield JJ (2019) Unsupervised learning by competing hidden units. Proc Natl Acad Sci USA","DOI":"10.1073\/pnas.1820458116"},{"key":"1595_CR21","doi-asserted-by":"crossref","unstructured":"Demircigil M, Heusel J, L\u00f6we M, Upgang S, Vermet F (2017) On a model of associative memory with huge storage capacity. J Stat Phys","DOI":"10.1007\/s10955-017-1806-y"},{"key":"1595_CR22","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.neunet.2019.01.004","volume":"113","author":"J Liu","year":"2019","unstructured":"Liu J, Gong M, He H (2019) Deep associative neural network for associative memory based on unsupervised representation learning. Neural Netw 113:41\u201353","journal-title":"Neural Netw"},{"key":"1595_CR23","doi-asserted-by":"crossref","unstructured":"Palm G (2013) Neural associative memories and sparse coding. Neural Netw","DOI":"10.1016\/j.neunet.2012.08.013"},{"key":"1595_CR24","unstructured":"Krotov D, Hopfield JJ (2016) Dense associative memory for pattern recognition. Advances in Neural Information Processing Systems"},{"key":"1595_CR25","doi-asserted-by":"crossref","unstructured":"Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA","DOI":"10.1073\/pnas.79.8.2554"},{"key":"1595_CR26","doi-asserted-by":"crossref","unstructured":"Zhang L, Wang S, Liu B (2018) Deep learning for sentiment analysis: a survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery","DOI":"10.1002\/widm.1253"},{"key":"1595_CR27","doi-asserted-by":"crossref","unstructured":"Fellbaum C (2010) WordNet. In: Theory and Applications of Ontology: Computer Applications","DOI":"10.1007\/978-90-481-8847-5_10"},{"key":"1595_CR28","doi-asserted-by":"crossref","unstructured":"Cambria E, Olsher D, Rajagopal D (2014) SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Proceedings of the National Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v28i1.8928"},{"key":"1595_CR29","doi-asserted-by":"crossref","unstructured":"Carrillo-de Albornoz J, Vidal JR, Plaza L (2018) Feature engineering for sentiment analysis in e-health forums. PLoS ONE","DOI":"10.1371\/journal.pone.0207996"}],"container-title":["Personal and Ubiquitous Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00779-021-01595-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00779-021-01595-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00779-021-01595-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T23:01:58Z","timestamp":1744153318000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00779-021-01595-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,11]]},"references-count":29,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["1595"],"URL":"https:\/\/doi.org\/10.1007\/s00779-021-01595-4","relation":{},"ISSN":["1617-4909","1617-4917"],"issn-type":[{"value":"1617-4909","type":"print"},{"value":"1617-4917","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,11]]},"assertion":[{"value":"1 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interest"}}]}}