{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:44:23Z","timestamp":1787021063625,"version":"build-2736575974"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T00:00:00Z","timestamp":1685318400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T00:00:00Z","timestamp":1685318400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST-111-2218-E-006-009-MBK"],"award-info":[{"award-number":["MOST-111-2218-E-006-009-MBK"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Various attempts have been conducted to improve the performance of text-based sentiment analysis. These significant attempts have focused on text representation and model classifiers. This paper introduced a hybrid model based on the text representation and the classifier models, to address sentiment classification with various topics. The combination of BERT and a distilled version of BERT (DistilBERT) was selected in the representative vectors of the input sentences, while the combination of long short-term memory and temporal convolutional networks was taken to enhance the proposed model in understanding the semantics and context of each word. The experiment results showed that the proposed model outperformed various counterpart schemes in considered metrics. The reliability of the proposed model was confirmed in a mixed dataset containing nine topics.<\/jats:p>","DOI":"10.1186\/s40537-023-00782-9","type":"journal-article","created":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T06:03:21Z","timestamp":1685340201000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Sentiment analysis of Indonesian datasets based on a hybrid deep-learning strategy"],"prefix":"10.1186","volume":"10","author":[{"given":"Chih-Hsueh","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulin","family":"Nuha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,29]]},"reference":[{"issue":"11","key":"782_CR1","doi-asserted-by":"publisher","first-page":"4157","DOI":"10.3390\/s22114157","volume":"22","author":"NJ Prottasha","year":"2022","unstructured":"Prottasha NJ, Sami AA, Kowsher M, Murad SA, Bairagi AK, Masud M, Baz M. Transfer learning for sentiment analysis using bert based supervised fine-tuning. Sensors. 2022;22(11):4157.","journal-title":"Sensors"},{"key":"782_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106935","volume":"98","author":"B Ray","year":"2021","unstructured":"Ray B, Garain A, Sarkar R. An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews. Appl Soft Comput. 2021;98: 106935.","journal-title":"Appl Soft Comput"},{"issue":"3","key":"782_CR3","doi-asserted-by":"publisher","first-page":"411","DOI":"10.3390\/healthcare10030411","volume":"10","author":"AA Reshi","year":"2022","unstructured":"Reshi AA, Rustam F, Aljedaani W, Shafi S, Alhossan A, Alrabiah Z, Ahmad A, Alsuwailem H, Almangour TA, Alshammari MA, et al. COVID-19 vaccination-related sentiments analysis: a case study using worldwide twitter dataset. Healthcare. 2022;10(3):411.","journal-title":"Healthcare"},{"key":"782_CR4","doi-asserted-by":"crossref","unstructured":"Sousa MG, Sakiyama K, Rodrigues LDS, Moraes PH, Fernandes ER, Matsubara ET. BERT for stock market sentiment analysis. In: Proceedings of international conference on tools for artificial intelligence (ICTAI). IEEE; 2019. p. 1597\u2013601.","DOI":"10.1109\/ICTAI.2019.00231"},{"key":"782_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.aci.2019.11.003","author":"O Alqaryouti","year":"2019","unstructured":"Alqaryouti O, Siyam N, Monem AA, Shaalan K. Aspect-based sentiment analysis using smart government review data. Appl Comput Inform. 2019. https:\/\/doi.org\/10.1016\/j.aci.2019.11.003.","journal-title":"Appl Comput Inform"},{"issue":"6","key":"782_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/JOEUC.294580","volume":"33","author":"T Bao","year":"2021","unstructured":"Bao T, Ren N, Luo R, Wang B, Shen G, Guo T. A BERT-based hybrid short text classification model incorporating CNN and attention-based BiGRU. J Organ End User Comput. 2021;33(6):1\u201321.","journal-title":"J Organ End User Comput"},{"key":"782_CR7","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint. 2019. arXiv:1810.04805."},{"key":"782_CR8","doi-asserted-by":"crossref","unstructured":"Adoma AF, Henry NM, Chen W. Comparative analyses of Bert, Roberta, Distilbert, and Xlnet for text-based emotion recognition. In: 2020 17th international computer conference on wavelet active media technology and information processing (ICCWAMTIP). IEEE; 2020. p. 117\u201321.","DOI":"10.1109\/ICCWAMTIP51612.2020.9317379"},{"key":"782_CR9","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/9986920","author":"CN Dang","year":"2021","unstructured":"Dang CN, Garcia MNM, Prieta FDL. Hybrid deep learning models for sentiment analysis. Complexity. 2021. https:\/\/doi.org\/10.1155\/2021\/9986920.","journal-title":"Complexity"},{"key":"782_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116256","volume":"193","author":"Ankita","year":"2022","unstructured":"Ankita, Rani S, Bashir AK, Alhudhaif A, Koundal D, Gunduz ES. An efficient CNN-LSTM model for sentiment detection in #BlackLivesMatter. Expert Syst Appl. 2022;193: 116256.","journal-title":"Expert Syst Appl"},{"key":"782_CR11","doi-asserted-by":"crossref","unstructured":"Yaseen TB, Ismail Q, Omari SA, Sobh EA, Abdullah M. JUST-BLUE at SemEval-2021 task 1: predicting lexical complexity using BERT and RoBERTa pre-trained language models. In: Proceedings of the 15th international workshop on semantic evaluation (SemEval-2021). 2021. p. 661\u20136.","DOI":"10.18653\/v1\/2021.semeval-1.85"},{"key":"782_CR12","doi-asserted-by":"crossref","unstructured":"Zheng S, Yang M. A new method of improving BERT for text classification. In: International conference on intelligent science and big data engineering. Springer; 2019. p. 442\u201352.","DOI":"10.1007\/978-3-030-36204-1_37"},{"key":"782_CR13","unstructured":"Galeano SR. Using BERT encoding to tackle the mad-lib attack in SMS spam detection. arXiv preprint. 2021. arXiv:2107.06400."},{"key":"782_CR14","doi-asserted-by":"publisher","first-page":"012034","DOI":"10.1088\/1742-6596\/1631\/1\/012034","volume":"1631","author":"J Zheng","year":"2020","unstructured":"Zheng J, Wang J, Ren Y, Yang Z. Chinese sentiment analysis of online education and internet buzzwords based on BERT. J Phys Conf Ser. 2020;1631:012034.","journal-title":"J Phys Conf Ser"},{"key":"782_CR15","doi-asserted-by":"publisher","first-page":"21517","DOI":"10.1109\/ACCESS.2022.3152828","volume":"10","author":"KL Tan","year":"2022","unstructured":"Tan KL, Lee CP, Anbananthen KSM, Lim KM. RoBERTa-LSTM: a hybrid model for sentiment analysis with transformer and recurrent neural network. IEEE Access. 2022;10:21517\u201325.","journal-title":"IEEE Access"},{"issue":"6","key":"782_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102723","volume":"58","author":"M Samadi","year":"2021","unstructured":"Samadi M, Mousavian M, Momtazi S. Deep contextualized text representation and learning for fake news detection. Inf Proc Manag. 2021;58(6): 102723.","journal-title":"Inf Proc Manag"},{"key":"782_CR17","doi-asserted-by":"crossref","unstructured":"Liu L, An D, Wang Y, Ma X, Jiang C. Research on legal judgment prediction based on Bert and LSTM-CNN fusion model. In: Proceedings of 2021 3rd world symposium on artificial intelligence (WSAI). IEEE; 2021. p. 41\u20135.","DOI":"10.1109\/WSAI51899.2021.9486374"},{"key":"782_CR18","doi-asserted-by":"crossref","unstructured":"Li W, Gao S, Zhou H, Huang Z, Zhang K, Li W. The automatic text classification method based on BERT and feature union. In: Proceedings of international conference on parallel and distributed systems (ICPADS). IEEE; 2019. p. 774\u20137.","DOI":"10.1109\/ICPADS47876.2019.00114"},{"key":"782_CR19","doi-asserted-by":"crossref","unstructured":"Mohbey KK, Meena G, Kumar S, Lokesh K. A CNN-LSTM-based hybrid deep learning approach to detect sentiment polarities on Monkeypox tweets. arXiv preprint. 2022. arXiv:2208.12019v1.","DOI":"10.1007\/s00354-023-00227-0"},{"key":"782_CR20","doi-asserted-by":"crossref","unstructured":"Yang P, Gao W, Deng Y. Film review sentiment classification based on BiGRU and attention. In: Proceedings of international conference on machine learning, big data and business intelligence (MLBDBI). IEEE; 2021. p. 40\u20135.","DOI":"10.1109\/MLBDBI54094.2021.00016"},{"key":"782_CR21","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1007\/s42979-021-00993-y","volume":"3","author":"G Meena","year":"2022","unstructured":"Meena G, Mohbey KK, Indian A. Categorizing sentiment polarities in social networks data using convolutional neural network. SN Comput Sci. 2022;3:116.","journal-title":"SN Comput Sci"},{"issue":"1","key":"782_CR22","first-page":"1367","volume":"13","author":"VD Antonio","year":"2022","unstructured":"Antonio VD, Efendi S, Mawengkang H. Sentiment analysis for covid-19 in indonesia on twitter with TF-IDF featured extraction and stochastic gradient descent. Int J Nonliear Anal Appl. 2022;13(1):1367\u201373.","journal-title":"Int J Nonliear Anal Appl"},{"key":"782_CR23","doi-asserted-by":"crossref","unstructured":"Nugroho KS, Sukmadewa AY, Dw HW. BERT fine-tuning for sentiment analysis on Indonesian mobile apps reviews. In: SIET \u201921. ACM; 2021. p. 258\u201364.","DOI":"10.1145\/3479645.3479679"},{"key":"782_CR24","doi-asserted-by":"crossref","unstructured":"Firmoza D, Amalia A, Harumy THF. Sentiment analysis for movie review in Bahasa Indonesia using BERT. In: Proceedings of 2021 international conference on data science, artificial intelligence, and business analytics (DATABIA). IEEE; 2021. p. 27\u201334.","DOI":"10.1109\/DATABIA53375.2021.9650096"},{"issue":"1","key":"782_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102756","volume":"59","author":"J Briskilal","year":"2022","unstructured":"Briskilal J, Subalalitha CN. An ensemble model for classifying idioms and literal texts using BERT and RoBERTa. Inf Proc Manag. 2022;59(1): 102756.","journal-title":"Inf Proc Manag"},{"issue":"7","key":"782_CR26","doi-asserted-by":"publisher","DOI":"10.2196\/17832","volume":"8","author":"K Zeng","year":"2020","unstructured":"Zeng K, Pan Z, Xu Y, Qu Y. An ensemble learning strategy for eligibility criteria text classification for clinical trial recruitment: algorithm development and validation. JMIR Med Inform. 2020;8(7): e17832.","journal-title":"JMIR Med Inform"},{"key":"782_CR27","doi-asserted-by":"crossref","unstructured":"Setiani E, Ce W. Text classification services using Na\u00efve Bayes for Bahasa Indonesia. In: Proceedings of 2018 international conference on information management and technology (ICIMTech). IEEE; 2018. p. 361\u20136.","DOI":"10.1109\/ICIMTech.2018.8528258"},{"key":"782_CR28","doi-asserted-by":"crossref","unstructured":"Sebastian D, Nugraha KA. Text normalization for indonesian abbreviated word using crowdsourcing method. In: Proceedings of international conference on information and communications technology (ICOIACT). IEEE; 2019. p. 529\u201332.","DOI":"10.1109\/ICOIACT46704.2019.8938463"},{"key":"782_CR29","doi-asserted-by":"publisher","first-page":"5789","DOI":"10.1007\/s10462-021-09958-2","volume":"51","author":"FA Acheampong","year":"2021","unstructured":"Acheampong FA, Mensah HN, Chen W. Transformer models for text-based emotion detection: a review of BERT-based approaches. Artif Intel Rev. 2021;51:5789\u2013829.","journal-title":"Artif Intel Rev"},{"key":"782_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2023.107217","volume":"160","author":"L Gomes","year":"2023","unstructured":"Gomes L, Torres RDS, Cortes M. BERT- and TF-IDF-based feature extraction for long-lived bug prediction in FLOSS: a comparative study. Inf Softw Technol. 2023;160: 107217.","journal-title":"Inf Softw Technol"},{"key":"782_CR31","unstructured":"Sanh V, Debut L, Chaumond J, Wolf T. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint. 2020. arXiv:1910.01108."},{"issue":"4","key":"782_CR32","doi-asserted-by":"publisher","first-page":"477","DOI":"10.3390\/ai2040030","volume":"2","author":"MJ Hamayel","year":"2021","unstructured":"Hamayel MJ, Owda AY. A Novel cryptocurrency price prediction model using GRU, LSTM and bi-LSTM machine learning algorithms. AI. 2021;2(4):477\u201396.","journal-title":"AI"},{"issue":"7","key":"782_CR33","doi-asserted-by":"publisher","first-page":"2322","DOI":"10.3390\/app10072322","volume":"10","author":"PL Benitez","year":"2020","unstructured":"Benitez PL, Garcia MC, Romera JML, Riquelme JC. Temporal convolutional networks applied to energy-related time series forecasting. Appl Sci. 2020;10(7):2322.","journal-title":"Appl Sci"},{"key":"782_CR34","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/978-3-642-12538-6_6","volume-title":"Nature inspired cooperative strategies for optimization","author":"XS Yang","year":"2010","unstructured":"Yang XS. A new metaheuristic bat-inspired algorithm. In: Gonzalez JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N, editors. Nature inspired cooperative strategies for optimization. Berlin: Springer; 2010. p. 65\u201374."},{"issue":"3","key":"782_CR35","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1504\/IJBIC.2013.055093","volume":"5","author":"XS Yang","year":"2013","unstructured":"Yang XS, He X. Bat algorithm: literature review and applications. Int J Bio-inspir Comput. 2013;5(3):141\u20139.","journal-title":"Int J Bio-inspir Comput"},{"key":"782_CR36","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.engappai.2015.10.006","volume":"48","author":"E Osaba","year":"2016","unstructured":"Osaba E, Yang XS, Diaz F, Garcia PL, Carballedo R. An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems. Eng Appl Artif Intell. 2016;48:59\u201371.","journal-title":"Eng Appl Artif Intell"},{"issue":"13","key":"782_CR37","doi-asserted-by":"publisher","first-page":"3733","DOI":"10.3390\/s20133733","volume":"20","author":"J Kim","year":"2020","unstructured":"Kim J, Yoo Y. Sensor node activation using bat algorithm for connected target coverage in WSNs. Sensors. 2020;20(13):3733.","journal-title":"Sensors"},{"key":"782_CR38","doi-asserted-by":"publisher","first-page":"1795","DOI":"10.1007\/s10796-021-10195-9","volume":"24","author":"YS Lee","year":"2022","unstructured":"Lee YS, Bang CC. Framework for the classification of imbalanced structured data using under-sampling and convolutional neural network. Inf Syst Front. 2022;24:1795\u2013809.","journal-title":"Inf Syst Front"},{"key":"782_CR39","doi-asserted-by":"crossref","unstructured":"Senn S, Tlachac ML, Flores R, Rundensteiner E. Ensembles of BERT for depression classification. In: Proceedings of annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE; 2022. p. 4691\u20134.","DOI":"10.1109\/EMBC48229.2022.9871120"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-023-00782-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-023-00782-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-023-00782-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T05:03:21Z","timestamp":1729487001000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-023-00782-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,29]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["782"],"URL":"https:\/\/doi.org\/10.1186\/s40537-023-00782-9","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,29]]},"assertion":[{"value":"25 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors consent to the publication of this manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"88"}}