{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T15:39:04Z","timestamp":1769355544453,"version":"3.49.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s00521-024-09453-z","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T17:03:02Z","timestamp":1708707782000},"page":"7161-7185","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A robust classification approach to enhance clinic identification from Arabic health text"],"prefix":"10.1007","volume":"36","author":[{"given":"Shrouq","family":"Al-Fuqaha\u2019a","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0980-7559","authenticated-orcid":false,"given":"Nailah","family":"Al-Madi","sequence":"additional","affiliation":[]},{"given":"Bassam","family":"Hammo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"issue":"11","key":"9453_CR1","doi-asserted-by":"publisher","first-page":"e1196","DOI":"10.1016\/S2214-109X(18)30386-3","volume":"6","author":"ME Kruk","year":"2018","unstructured":"Kruk ME, Gage AD, Arsenault C, Jordan K, Leslie HH, Roder-DeWan S, Pate M (2018) High-quality health systems in the Sustainable Development Goals era: time for a revolution. Lancet Glob Health 6(11):e1196\u2013e1252. https:\/\/doi.org\/10.1016\/S2214-109X(18)30386-3","journal-title":"Lancet Glob Health"},{"issue":"3","key":"9453_CR2","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1109\/JBHI.2018.2834230","volume":"23","author":"EC Schiza","year":"2019","unstructured":"Schiza EC, Kyprianou TC, Petkov N, Schizas CN (2019) Proposal for an ehealth based ecosystem serving national healthcare. IEEE J Biomed Health Inform 23(3):1346\u20131357. https:\/\/doi.org\/10.1109\/JBHI.2018.2834230","journal-title":"IEEE J Biomed Health Inform"},{"key":"9453_CR3","unstructured":"Altibbi. https:\/\/altibbi.com\/"},{"key":"9453_CR4","doi-asserted-by":"publisher","unstructured":"Lavanya, P. M., & Sasikala, E. (2021). Deep learning techniques on text classification using natural language processing (NLP) in social healthcare network: a comprehensive survey. In: 2021 3rd international conference on signal processing and communication (ICPSC) (pp. 603\u2013609). https:\/\/doi.org\/10.1109\/ICSPC51351.2021.9451752","DOI":"10.1109\/ICSPC51351.2021.9451752"},{"issue":"1","key":"9453_CR5","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1038\/s41746-022-00742-2","volume":"5","author":"X Yang","year":"2022","unstructured":"Yang X, Chen A, PourNejatian N et al (2022) A large language model for electronic health records. NPJ Digit Med 5(1):194. https:\/\/doi.org\/10.1038\/s41746-022-00742-2","journal-title":"NPJ Digit Med"},{"issue":"4","key":"9453_CR6","doi-asserted-by":"publisher","first-page":"150","DOI":"10.3390\/info10040150","volume":"10","author":"K Kowsari","year":"2019","unstructured":"Kowsari K, Meimandi KJ, Heidarysafa M, Mendu S, Barnes L, Brown D (2019) Text classification algorithms: a survey. Information 10(4):150. https:\/\/doi.org\/10.3390\/info10040150","journal-title":"Information"},{"key":"9453_CR7","doi-asserted-by":"crossref","unstructured":"Rusli A, Young J, Iswari N (2020) Identifying fake news in Indonesian via supervised binary text classification. In: 2020 IEEE international conference on industry 4.0, artificial intelligence, and communications technology (IAICT), pp 86\u201390","DOI":"10.1109\/IAICT50021.2020.9172020"},{"key":"9453_CR8","doi-asserted-by":"publisher","unstructured":"Joshi S, Abdelfattah E (2021) Multi-class text classification using machine learning models for online drug reviews. In: 2021 IEEE world AI IoT congress (AIIoT), pp 262\u2013267. https:\/\/doi.org\/10.1109\/AIIoT52608.2021.9454250","DOI":"10.1109\/AIIoT52608.2021.9454250"},{"key":"9453_CR9","doi-asserted-by":"crossref","unstructured":"Akhand B, Susheela Devi V (2013) Multi label classification of discrete data. In: 2013 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1\u20135","DOI":"10.1109\/FUZZ-IEEE.2013.6622574"},{"key":"9453_CR10","doi-asserted-by":"crossref","unstructured":"Chen X, Bromuri S, Tan DS (2022) Confidence range: bridging failure detection and true class probability on selective hierarchical text classification. Available at SSRN 4244490","DOI":"10.2139\/ssrn.4244490"},{"issue":"1","key":"9453_CR11","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1145\/1882471.1882478","volume":"12","author":"Z Xing","year":"2010","unstructured":"Xing Z, Pei J, Keogh E (2010) A brief survey on sequence classification. SIGKDD Explor 12(1):40\u201348","journal-title":"SIGKDD Explor"},{"key":"9453_CR12","doi-asserted-by":"crossref","unstructured":"Dhar A, Dash N, Roy K (2017) Classification of text documents through distance measurement: an experiment with multi-domain Bangla text documents. In: Proceedings of the 6th international conference on informatics, electronics and vision (ICIEV), pp 377\u2013382","DOI":"10.1109\/ICACCAF.2017.8344721"},{"issue":"6245","key":"9453_CR13","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1126\/science.aaa8685","volume":"349","author":"J Hirschberg","year":"2015","unstructured":"Hirschberg J, Manning CD (2015) Advances in natural language processing. Science 349(6245):261\u2013266","journal-title":"Science"},{"issue":"3","key":"9453_CR14","doi-asserted-by":"publisher","first-page":"2758","DOI":"10.1016\/j.eswa.2010.08.066","volume":"38","author":"W Zhang","year":"2011","unstructured":"Zhang W, Yoshida T, Tang X (2011) A comparative study of TF*IDF, LSI and multi-words for text classification. Expert Syst Appl 38(3):2758\u20132765. https:\/\/doi.org\/10.1016\/j.eswa.2010.08.066","journal-title":"Expert Syst Appl"},{"key":"9453_CR15","doi-asserted-by":"crossref","unstructured":"Sivakumar S, Videla L, Rajesh Kumar T, Nagaraj J, Itnal S, Haritha D (2020) Review on Word2Vec word embedding neural net. In: 2020 international conference on smart electronics and communication (ICOSEC), pp 282\u2013290","DOI":"10.1109\/ICOSEC49089.2020.9215319"},{"key":"9453_CR16","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv:1301.3781"},{"key":"9453_CR17","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2018) BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805"},{"key":"9453_CR18","unstructured":"TechTarget (n.d.) Recurrent neural networks (RNNs) definition. https:\/\/www.techtarget.com\/searchenterpriseai\/definition\/recurrent-neural-networks. Accessed 7 March 2023"},{"key":"9453_CR19","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A, Kaiser L, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30:5998\u20136008","journal-title":"Adv Neural Inf Process Syst"},{"key":"9453_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/7937667","volume":"2022","author":"AY Muaad","year":"2022","unstructured":"Muaad AY, Davanagere H, Benifa JV, Alabrah A, Saif M, Pushpa D, Alfaqih T (2022) Artificial intelligence-based approach for misogyny and sarcasm detection from Arabic texts. Comput Intell Neurosci 2022:1\u20139. https:\/\/doi.org\/10.1155\/2022\/7937667","journal-title":"Comput Intell Neurosci"},{"key":"9453_CR21","unstructured":"Al-antari MA, Muaad AY, Davanagere H, Benifa JB, Chola C (2021) AI-based misogyny detection from Arabic levantine twitter tweets, vol 2"},{"key":"9453_CR22","first-page":"4219","volume":"12","author":"RMS Ahmed","year":"2021","unstructured":"Ahmed RMS (2021) Fake news detection in low-resourced languages \u2018Kurdish language\u2019 using machine learning algorithms. Turki J Comput Math Education (TURCOMAT) 12:4219\u20134225","journal-title":"Turki J Comput Math Education (TURCOMAT)"},{"key":"9453_CR23","unstructured":"Althabiti S, Alsalka M, Atwell E (2021). SCUoL at CheckThat! 2021: an AraBERT model for check-worthiness of Arabic tweets. In: Proceedings of the 3rd workshop on fact extraction and verification (FEVER) shared task, pp 1025\u20131030"},{"key":"9453_CR24","doi-asserted-by":"publisher","unstructured":"Tahssin R, Kishk Y, Torki M (2020) Identifying nuanced dialect for Arabic tweets with deep learning and reverse translation corpus extension system. https:\/\/doi.org\/10.13140\/RG.2.2.31541.91363","DOI":"10.13140\/RG.2.2.31541.91363"},{"issue":"23","key":"9453_CR25","doi-asserted-by":"publisher","first-page":"13388","DOI":"10.3390\/app112311328","volume":"11","author":"N Essam","year":"2021","unstructured":"Essam N, Moussa AM, Elsayed KM, Abdou S, Rashwan M, Khatoon S, Alshamari MA (2021) Location analysis for Arabic COVID-19 twitter data using enhanced dialect identification models. Appl Sci 11(23):13388. https:\/\/doi.org\/10.3390\/app112311328","journal-title":"Appl Sci"},{"issue":"24","key":"9453_CR26","doi-asserted-by":"publisher","first-page":"13388","DOI":"10.3390\/ijerph182413388","volume":"18","author":"S Binkheder","year":"2021","unstructured":"Binkheder S, Aldekhyyel RN, AlMogbel A, Al-Twairesh N, Alhumaid N, Aldekhyyel SN, Jamal AA (2021) Public perceptions around mHealth applications during COVID-19 pandemic: a network and sentiment analysis of tweets in Saudi Arabia. Int J Environ Res Public Health 18(24):13388. https:\/\/doi.org\/10.3390\/ijerph182413388","journal-title":"Int J Environ Res Public Health"},{"issue":"2","key":"9453_CR27","doi-asserted-by":"publisher","first-page":"84","DOI":"10.3390\/info12020084","volume":"12","author":"N Al-Twairesh","year":"2021","unstructured":"Al-Twairesh N (2021) The evolution of language models applied to emotion analysis of Arabic tweets. Information 12(2):84. https:\/\/doi.org\/10.3390\/info12020084","journal-title":"Information"},{"issue":"4","key":"9453_CR28","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3390\/asi4040085","volume":"4","author":"HS Sharaf Al-deen","year":"2021","unstructured":"Sharaf Al-deen HS, Zeng Z, Al-sabri R, Hekmat A (2021) An improved model for analyzing textual sentiment based on a deep neural network using multi-head attention mechanism. Appl Syst Innov 4(4):85","journal-title":"Appl Syst Innov"},{"key":"9453_CR29","doi-asserted-by":"publisher","first-page":"12334","DOI":"10.1109\/ACCESS.2022.3146712","volume":"10","author":"AM Almars","year":"2022","unstructured":"Almars AM, Almaliki M, Noor TH, Alwateer MM, Atlam E (2022) HANN: hybrid attention neural network for detecting covid-19 related rumors. IEEE Access 10:12334\u201312344. https:\/\/doi.org\/10.1109\/ACCESS.2022.3146712","journal-title":"IEEE Access"},{"issue":"4","key":"9453_CR30","doi-asserted-by":"publisher","first-page":"3","DOI":"10.15676\/ijeei.2021.13.4.3","volume":"13","author":"N Habbat","year":"2022","unstructured":"Habbat N, Anoun H, Hassouni L (2022) A novel hybrid network for arabic sentiment analysis using fine-tuned AraBERT model. Int J Electr Eng Inform 13(4):3. https:\/\/doi.org\/10.15676\/ijeei.2021.13.4.3","journal-title":"Int J Electr Eng Inform"},{"key":"9453_CR31","doi-asserted-by":"publisher","first-page":"137176","DOI":"10.1109\/ACCESS.2021.3114313","volume":"9","author":"AA Wazrah","year":"2021","unstructured":"Wazrah AA, Alhumoud S, Saleh M, Ali A (2021) Sentiment analysis using stacked gated recurrent unit for Arabic tweets. IEEE Access 9:137176\u2013137187. https:\/\/doi.org\/10.1109\/ACCESS.2021.3114313","journal-title":"IEEE Access"},{"issue":"3","key":"9453_CR32","doi-asserted-by":"publisher","first-page":"439","DOI":"10.3390\/pr10030439","volume":"10","author":"AS Alhassun","year":"2022","unstructured":"Alhassun AS, Rassam MA (2022) A combined text-based and metadata-based deep-learning framework for the detection of spam accounts on the social media platform twitter. Processes 10(3):439. https:\/\/doi.org\/10.3390\/pr10030439","journal-title":"Processes"},{"key":"9453_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/1566890","volume":"2022","author":"Y Wazery","year":"2022","unstructured":"Wazery Y, Saleh M, Alharbi A, Ali A (2022) Abstractive Arabic text summarization based on deep learning. Comput Intell Neurosci 2022:1\u201314. https:\/\/doi.org\/10.1155\/2022\/1566890","journal-title":"Comput Intell Neurosci"},{"key":"9453_CR34","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2022.01304112","author":"A Mohamed","year":"2022","unstructured":"Mohamed A, Rady S, Gharib T (2022) An ensemble deep learning approach for emotion detection in Arabic tweets. Int J Adv Comput Sci Appl. https:\/\/doi.org\/10.14569\/IJACSA.2022.01304112","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"22","key":"9453_CR35","doi-asserted-by":"publisher","first-page":"10694","DOI":"10.3390\/app112210694","volume":"11","author":"N Alturayeif","year":"2021","unstructured":"Alturayeif N, Luqman H (2021) Fine-grained sentiment analysis of Arabic COVID-19 tweets using BERT-based transformers and dynamically weighted loss function. Appl Sci 11(22):10694. https:\/\/doi.org\/10.3390\/app112210694","journal-title":"Appl Sci"},{"key":"9453_CR36","doi-asserted-by":"crossref","unstructured":"Alghanmi I, Anke LE, Schockaert S (2020) Combining BERT with static word embeddings for categorizing social media. In: Proceedings of the sixth workshop on noisy user-generated text (WNUT)","DOI":"10.18653\/v1\/2020.wnut-1.5"},{"key":"9453_CR37","doi-asserted-by":"publisher","first-page":"30526","DOI":"10.1109\/ACCESS.2022.3159252","volume":"10","author":"AS Fadel","year":"2022","unstructured":"Fadel AS, Saleh ME, Abulnaja OA (2022) Arabic aspect extraction based on stacked contextualized embedding with deep learning. IEEE Access 10:30526\u201330535. https:\/\/doi.org\/10.1109\/ACCESS.2022.3159252","journal-title":"IEEE Access"},{"key":"9453_CR38","doi-asserted-by":"publisher","unstructured":"Abdullah M, Alnore D, Swedat S, Khrais J, Al-Ayyoub M (2022). SarcasmDet at SemEval-2022 task 6: detecting sarcasm using pre-trained transformers in English and Arabic Languages. In: Proceedings of the 16th international workshop on semantic evaluation (SemEval-2022), pp 885\u2013890. https:\/\/doi.org\/10.18653\/v1\/2022.semeval-1.124","DOI":"10.18653\/v1\/2022.semeval-1.124"},{"key":"9453_CR39","doi-asserted-by":"publisher","unstructured":"Al-Qarqaz A, Abujaber D, Abdullah M (2021) R00 at NLP4IF-2021: fighting COVID-19 infodemic with transformers and more transformers. In: Proceedings of the 7th workshop on NLP for internet freedom (NLP4IF), pp 93\u201398. https:\/\/doi.org\/10.18653\/v1\/2021.nlp4if-1.15","DOI":"10.18653\/v1\/2021.nlp4if-1.15"},{"key":"9453_CR40","doi-asserted-by":"publisher","first-page":"111214","DOI":"10.1109\/ACCESS.2021.3102087","volume":"9","author":"H Elfaik","year":"2021","unstructured":"Elfaik H, Nfaoui EH (2021) Combining context-aware embeddings and an attentional deep learning model for Arabic affect analysis on twitter. IEEE Access 9:111214\u2013111230. https:\/\/doi.org\/10.1109\/ACCESS.2021.3102087","journal-title":"IEEE Access"},{"key":"9453_CR41","doi-asserted-by":"publisher","unstructured":"Mohamed Kamr A, Mohamed E (2022) akaBERT at SemEval-2022 task 6: an ensemble transformer-based model for Arabic sarcasm detection. In: Proceedings of the 16th international workshop on semantic evaluation (SemEval-2022), pp 885\u2013890. https:\/\/doi.org\/10.18653\/v1\/2022.semeval-1.124","DOI":"10.18653\/v1\/2022.semeval-1.124"},{"key":"9453_CR42","doi-asserted-by":"crossref","unstructured":"Hussein A, Ghneim N, Joukhadar A (2021) DamascusTeam at NLP4IF2021: fighting the Arabic COVID-19 infodemic on Twitter using AraBERT. In: Proceedings of the 7th workshop on NLP for internet freedom (NLP4IF), pp 99\u2013104","DOI":"10.18653\/v1\/2021.nlp4if-1.13"},{"key":"9453_CR43","doi-asserted-by":"publisher","unstructured":"Akkem Y, Biswas SK, Varanasi A (2023) Smart farming monitoring using ML and MLOps. In: International conference on innovative computing and communications, ICICC 2023, pp 665\u2013675. https:\/\/doi.org\/10.1007\/978-981-99-3315-0_51","DOI":"10.1007\/978-981-99-3315-0_51"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09453-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09453-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09453-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T13:20:53Z","timestamp":1711027253000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09453-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,23]]},"references-count":43,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["9453"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09453-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,23]]},"assertion":[{"value":"27 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2024","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 that they have no known competing financial interests or personal relations that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}