{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T12:33:53Z","timestamp":1781613233018,"version":"3.54.5"},"reference-count":95,"publisher":"Springer Science and Business Media LLC","issue":"39","license":[{"start":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T00:00:00Z","timestamp":1723248000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T00:00:00Z","timestamp":1723248000000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19965-4","type":"journal-article","created":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T03:32:18Z","timestamp":1723260738000},"page":"87323-87367","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Improving emotion classification in e-commerce customer review analysis using GPT and meta\u2011ensemble deep learning technique for multilingual system"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4055-0616","authenticated-orcid":false,"given":"Nouri","family":"Hicham","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Habbat","family":"Nassera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,10]]},"reference":[{"issue":"2","key":"19965_CR1","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1007\/s12065-020-00429-1","volume":"14","author":"A Naresh","year":"2021","unstructured":"Naresh A, Venkata Krishna P (2021) An efficient approach for sentiment analysis using machine learning algorithm. Evol Intel 14(2):725\u201331. https:\/\/doi.org\/10.1007\/s12065-020-00429-1","journal-title":"Evol Intel"},{"issue":"3","key":"19965_CR2","doi-asserted-by":"publisher","first-page":"790","DOI":"10.18280\/mmep.100308","volume":"10","author":"N Hicham","year":"2023","unstructured":"Hicham N, Karim S, Habbat N (2023) Enhancing Arabic Sentiment Analysis in E-Commerce Reviews on Social Media Through a Stacked Ensemble Deep Learning Approach. MMEP 10(3):790\u2013798. https:\/\/doi.org\/10.18280\/mmep.100308","journal-title":"MMEP"},{"issue":"15","key":"19965_CR3","doi-asserted-by":"publisher","first-page":"22613","DOI":"10.1007\/s11042-023-14432-y","volume":"82","author":"R Jain","year":"2023","unstructured":"Jain R et al (2023) Explaining sentiment analysis results on social media texts through visualization. Multimed Tools Appl 82(15):22613\u201322629. https:\/\/doi.org\/10.1007\/s11042-023-14432-y","journal-title":"Multimed Tools Appl"},{"key":"19965_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-15213-3","author":"R Jain","year":"2023","unstructured":"Jain R, Rai RS, Jain S, Ahluwalia R, Gupta J (2023) Real time sentiment analysis of natural language using multimedia input. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-023-15213-3","journal-title":"Multimed Tools Appl"},{"key":"19965_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-17011-3","author":"L Dang","year":"2023","unstructured":"Dang L, Wang C, Tsou M-H, Hou Y, Han H (2023) Sentiment analysis of COVID-19 related social distancing using twitter data based on deep learning. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-023-17011-3","journal-title":"Multimed Tools Appl"},{"key":"19965_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-15762-7","author":"Y Luo","year":"2023","unstructured":"Luo Y, Wu R, Liu J, Tang X (2023) Attention fusion network for multimodal sentiment analysis. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-023-15762-7","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"19965_CR7","doi-asserted-by":"publisher","first-page":"4504","DOI":"10.11591\/ijece.v13i4.pp4504-4515","volume":"13","author":"N Hicham","year":"2023","unstructured":"Hicham N, Karim S, Habbat N (2023) Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach. IJECE 13(4):4504. https:\/\/doi.org\/10.11591\/ijece.v13i4.pp4504-4515","journal-title":"IJECE"},{"key":"19965_CR8","doi-asserted-by":"publisher","unstructured":"Hicham N, Karim S (2023) \u201cMachine Learning Applications for Consumer Behavior Prediction,\u201d Lecture Notes in Networks and Systems, vol. 629 LNNS. pp. 666\u2013675, 2023. https:\/\/doi.org\/10.1007\/978-3-031-26852-6_62.","DOI":"10.1007\/978-3-031-26852-6_62"},{"issue":"1","key":"19965_CR9","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/s13278-019-0596-4","volume":"9","author":"A Mohammed","year":"2019","unstructured":"Mohammed A, Kora R (2019) Deep learning approaches for Arabic sentiment analysis. Soc Netw Anal Min 9(1):52. https:\/\/doi.org\/10.1007\/s13278-019-0596-4","journal-title":"Soc Netw Anal Min"},{"issue":"1","key":"19965_CR10","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1007\/s13278-023-01043-6","volume":"13","author":"R Kora","year":"2023","unstructured":"Kora R, Mohammed A (2023) An enhanced approach for sentiment analysis based on meta-ensemble deep learning. Soc Netw Anal Min 13(1):38. https:\/\/doi.org\/10.1007\/s13278-023-01043-6","journal-title":"Soc Netw Anal Min"},{"key":"19965_CR11","doi-asserted-by":"publisher","unstructured":"Chen Y, Yuan J, You Q, Luo J (2018) \u201cTwitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM,\u201d in Proceedings of the 26th ACM international conference on Multimedia, Seoul Republic of Korea: ACM, pp. 117\u2013125. https:\/\/doi.org\/10.1145\/3240508.3240533.","DOI":"10.1145\/3240508.3240533"},{"issue":"2\u20133","key":"19965_CR12","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1080\/12460125.2020.1864106","volume":"30","author":"ES Alamoudi","year":"2021","unstructured":"Alamoudi ES, Alghamdi NS (2021) Sentiment classification and aspect-based sentiment analysis on yelp reviews using deep learning and word embeddings. J Decis Syst 30(2\u20133):259\u2013281. https:\/\/doi.org\/10.1080\/12460125.2020.1864106","journal-title":"J Decis Syst"},{"issue":"1","key":"19965_CR13","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1186\/s12859-019-2972-5","volume":"20","author":"NQK Le","year":"2019","unstructured":"Le NQK, Yapp EKY, Yeh H-Y (2019) ET-GRU: using multi-layer gated recurrent units to identify electron transport proteins. BMC Bioinformatics 20(1):377. https:\/\/doi.org\/10.1186\/s12859-019-2972-5","journal-title":"BMC Bioinformatics"},{"issue":"4","key":"19965_CR14","first-page":"801","volume":"13","author":"N Habbat","year":"2021","unstructured":"Habbat N, Anoun H, Hassouni L (2021) A Novel Hybrid Network for Arabic Sentiment Analysis using fine-tuned AraBERT model. Int J Electr Eng Inf 13(4):801\u201312","journal-title":"Int J Electr Eng Inf"},{"issue":"6","key":"19965_CR15","doi-asserted-by":"publisher","first-page":"4335","DOI":"10.1007\/s10462-019-09794-5","volume":"53","author":"A Yadav","year":"2020","unstructured":"Yadav A, Vishwakarma DK (2020) Sentiment analysis using deep learning architectures: a review. Artif Intell Rev 53(6):4335\u20134385. https:\/\/doi.org\/10.1007\/s10462-019-09794-5","journal-title":"Artif Intell Rev"},{"key":"19965_CR16","doi-asserted-by":"publisher","unstructured":"Sivaraman, Arun Kumar, Vincent, Rajiv, Bhatia, Prayag, Rajesh, M., and Bahri, Mohammed Said Sulaiman Al, \u201cIndian Currency Recognition and Verification using Transfer Learning,\u201d p. 59610 Bytes, 2021, https:\/\/doi.org\/10.6084\/M9.FIGSHARE.16944082.","DOI":"10.6084\/M9.FIGSHARE.16944082"},{"issue":"4","key":"19965_CR17","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1016\/j.jretai.2020.12.001","volume":"97","author":"XS Wang","year":"2021","unstructured":"Wang XS, Ryoo JH, Bendle N, Kopalle PK (2021) The role of machine learning analytics and metrics in retailing research. J Retal 97(4):658\u201375. https:\/\/doi.org\/10.1016\/j.jretai.2020.12.001","journal-title":"J Retal"},{"key":"19965_CR18","doi-asserted-by":"publisher","unstructured":"Ganga M, Janakiraman N, Sivaraman AK, Balasundaram A, Vincent R, Rajesh M (2021) Survey of texture based image processing and analysis with differential fractional calculus methods. In2021 International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1-6). IEEE. https:\/\/doi.org\/10.1109\/ICSCAN53069.2021.9526439.","DOI":"10.1109\/ICSCAN53069.2021.9526439"},{"key":"19965_CR19","unstructured":"Stamatatos E, Widmer G (2002) \u201cMusic Performer Recognition Using an Ensemble of Simple Classifiers\u201d."},{"issue":"1","key":"19965_CR20","first-page":"98","volume":"4","author":"RK Shahzad","year":"2013","unstructured":"Shahzad RK, Lavesson N (2013) Comparative Analysis of Voting Schemes for Ensemble-based Malware Detection. J Wirel Mobile Networks, Ubiquit Comput, Dependable Appl 4(1):98\u2013117","journal-title":"J Wirel Mobile Networks, Ubiquit Comput, Dependable Appl"},{"key":"19965_CR21","doi-asserted-by":"publisher","unstructured":"Asogwa DC, Anigbogu SO, Onyenwe IE, Sani FA (2021) Text classification using hybrid machine learning algorithms on big data. arXiv preprint arXiv:2103.16624. https:\/\/doi.org\/10.48550\/ARXIV.2103.16624","DOI":"10.48550\/ARXIV.2103.16624"},{"key":"19965_CR22","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s42979-021-00922-z","volume":"3","author":"MMR Mamun","year":"2022","unstructured":"Mamun MMR, Sharif O, Hoque MM (2022) Classification of Textual Sentiment Using Ensemble Technique. SN Comput Sci 3:49","journal-title":"SN Comput Sci"},{"key":"19965_CR23","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 (2022) RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network. IEEE Access 10:21517\u201321525. https:\/\/doi.org\/10.1109\/ACCESS.2022.3152828","journal-title":"IEEE Access"},{"issue":"22","key":"19965_CR24","doi-asserted-by":"publisher","first-page":"15541","DOI":"10.1007\/s00521-021-06177-2","volume":"33","author":"E Tasci","year":"2021","unstructured":"Tasci E, Uluturk C, Ugur A (2021) A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection. Neural Comput & Applic 33(22):15541\u201315555. https:\/\/doi.org\/10.1007\/s00521-021-06177-2","journal-title":"Neural Comput & Applic"},{"key":"19965_CR25","doi-asserted-by":"publisher","unstructured":"Rajabi Z, Shehu A, Uzuner O (2020) \u201cA Multi-channel BiLSTM-CNN Model for Multilabel Emotion Classification of Informal Text,\u201d in 2020 IEEE 14th International Conference on Semantic Computing (ICSC), San Diego, CA, USA: IEEE, pp. 303\u2013306. https:\/\/doi.org\/10.1109\/ICSC.2020.00060.","DOI":"10.1109\/ICSC.2020.00060"},{"key":"19965_CR26","doi-asserted-by":"publisher","unstructured":"Xin Li et al (2016) \u201cWeighted multi-label classification model for sentiment analysis of online news,\u201d in 2016 International Conference on Big Data and Smart Computing (BigComp), Hong Kong, China: IEEE, pp. 215\u2013222. https:\/\/doi.org\/10.1109\/BIGCOMP.2016.7425916.","DOI":"10.1109\/BIGCOMP.2016.7425916"},{"key":"19965_CR27","doi-asserted-by":"publisher","first-page":"1839","DOI":"10.1109\/TASLP.2020.3001390","volume":"28","author":"H Fei","year":"2020","unstructured":"Fei H, Ji D, Zhang Y, Ren Y (2020) Topic-Enhanced Capsule Network for Multi-Label Emotion Classification. IEEE\/ACM Trans Audio Speech Lang Process 28:1839\u20131848. https:\/\/doi.org\/10.1109\/TASLP.2020.3001390","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"19965_CR28","doi-asserted-by":"publisher","unstructured":"Hu X, Yang Y, Chen L, Zhu S (2020) \u201cResearch on a Prediction Model of Online Shopping Behavior Based on Deep Forest Algorithm,\u201d in 2020 3rd International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China: IEEE, pp. 137\u2013141. https:\/\/doi.org\/10.1109\/ICAIBD49809.2020.9137436.","DOI":"10.1109\/ICAIBD49809.2020.9137436"},{"key":"19965_CR29","doi-asserted-by":"publisher","unstructured":"Tasmin M (2018) Multi-dimensional aspect analysis of text input through human emotion and social factors. InProceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (pp. 1779-1781). https:\/\/doi.org\/10.1145\/3267305.3277817.","DOI":"10.1145\/3267305.3277817"},{"key":"19965_CR30","doi-asserted-by":"publisher","unstructured":"Yan D, Hu B, Qin J (2018) \u201cSentiment Analysis for Microblog Related to Finance Based on Rules and Classification,\u201d in 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai: IEEE, pp. 119\u2013126. https:\/\/doi.org\/10.1109\/BigComp.2018.00026.","DOI":"10.1109\/BigComp.2018.00026"},{"key":"19965_CR31","doi-asserted-by":"publisher","unstructured":"Liu H, Guo H, Hu W (2021) \u201cEEG-Based Emotion Classification Using Joint Adaptation Networks,\u201d in 2021 IEEE International Symposium on Circuits and Systems (ISCAS), Daegu, Korea: IEEE, pp. 1\u20135. https:\/\/doi.org\/10.1109\/ISCAS51556.2021.9401737.","DOI":"10.1109\/ISCAS51556.2021.9401737"},{"issue":"7","key":"19965_CR32","doi-asserted-by":"publisher","first-page":"2074","DOI":"10.3390\/s18072074","volume":"18","author":"L Shu","year":"2018","unstructured":"Shu L et al (2018) A Review of Emotion Recognition Using Physiological Signals. Sensors 18(7):2074. https:\/\/doi.org\/10.3390\/s18072074","journal-title":"Sensors"},{"issue":"1","key":"19965_CR33","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/s11036-020-01697-y","volume":"26","author":"Y Tang","year":"2021","unstructured":"Tang Y, Su J, Khan MA (2021) Research on Sentiment Analysis of Network Forum Based on BP Neural Network. Mobile Netw Appl 26(1):174\u2013183. https:\/\/doi.org\/10.1007\/s11036-020-01697-y","journal-title":"Mobile Netw Appl"},{"key":"19965_CR34","doi-asserted-by":"publisher","unstructured":"Hicham N, Karim S, Habbat N (2022) \u201cAn efficient approach for improving customer Sentiment Analysis in the Arabic language using an Ensemble machine learning technique,\u201d in 2022 5th International Conference on Advanced Communication Technologies and Networking (CommNet), pp. 1\u20136. https:\/\/doi.org\/10.1109\/CommNet56067.2022.9993924.","DOI":"10.1109\/CommNet56067.2022.9993924"},{"issue":"3","key":"19965_CR35","doi-asserted-by":"publisher","first-page":"4675","DOI":"10.32604\/cmc.2022.027311","volume":"72","author":"M Hadwan","year":"2022","unstructured":"Hadwan M, Al-Hagery MA, Al-Sarem M, Saeed F (2022) Arabic Sentiment Analysis of Users\u2019 Opinions of Governmental Mobile Applications. Computers, Materials & Continua 72(3):4675\u20134689. https:\/\/doi.org\/10.32604\/cmc.2022.027311","journal-title":"Computers, Materials & Continua"},{"issue":"14","key":"19965_CR36","first-page":"77","volume":"5","author":"N Omar","year":"2013","unstructured":"Omar N, Albared M, Al-Shabi AQ, Al-Moslmi T (2013) Ensemble of classification algorithms for subjectivity and sentiment analysis of Arabic customers\u2019 reviews. Int J Adv Comput Technol 5(14):77","journal-title":"Int J Adv Comput Technol"},{"issue":"1","key":"19965_CR37","doi-asserted-by":"publisher","first-page":"102121","DOI":"10.1016\/j.ipm.2019.102121","volume":"57","author":"A Elnagar","year":"2020","unstructured":"Elnagar A, Al-Debsi R, Einea O (2020) Arabic text classification using deep learning models. Inf Process Manage 57(1):102121. https:\/\/doi.org\/10.1016\/j.ipm.2019.102121","journal-title":"Inf Process Manage"},{"issue":"2","key":"19965_CR38","doi-asserted-by":"publisher","first-page":"102438","DOI":"10.1016\/j.ipm.2020.102438","volume":"58","author":"IA Farha","year":"2021","unstructured":"Farha IA, Magdy W (2021) A comparative study of effective approaches for Arabic sentiment analysis. Inf Process Manage 58(2):102438. https:\/\/doi.org\/10.1016\/j.ipm.2020.102438","journal-title":"Inf Process Manage"},{"issue":"2","key":"19965_CR39","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1162\/neco.1989.1.2.270","volume":"1","author":"RJ Williams","year":"1989","unstructured":"Williams RJ, Zipser D (1989) A Learning Algorithm for Continually Running Fully Recurrent Neural Networks. Neural Comput 1(2):270\u2013280. https:\/\/doi.org\/10.1162\/neco.1989.1.2.270","journal-title":"Neural Comput"},{"key":"19965_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/5538791","volume":"2021","author":"M Mhamed","year":"2021","unstructured":"Mhamed M, Sutcliffe R, Sun X, Feng J, Almekhlafi E, Retta EA (2021) Improving Arabic Sentiment Analysis Using CNN-Based Architectures and Text Preprocessing. Comput Intell Neurosci 2021:1\u201312. https:\/\/doi.org\/10.1155\/2021\/5538791","journal-title":"Comput Intell Neurosci"},{"issue":"9","key":"19965_CR41","doi-asserted-by":"publisher","first-page":"6652","DOI":"10.1016\/j.jksuci.2021.08.030","volume":"34","author":"MM Abdelgwad","year":"2022","unstructured":"Abdelgwad MM, Soliman TH, Taloba AI, Farghaly MF (2022) Arabic aspect based sentiment analysis using bidirectional GRU based models. J King Saud Univ-Comput Inf Sci 34(9):6652\u201362. https:\/\/doi.org\/10.1016\/j.jksuci.2021.08.030","journal-title":"J King Saud Univ-Comput Inf Sci"},{"issue":"8","key":"19965_CR42","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.3390\/electronics11081193","volume":"11","author":"M Alali","year":"2022","unstructured":"Alali M, MohdSharef N, Azmi Murad MA, Hamdan H, Husin NA (2022) Multitasking Learning Model Based on Hierarchical Attention Network for Arabic Sentiment Analysis Classification. Electronics 11(8):1193. https:\/\/doi.org\/10.3390\/electronics11081193","journal-title":"Electronics"},{"issue":"4","key":"19965_CR43","doi-asserted-by":"publisher","first-page":"e1249","DOI":"10.1002\/widm.1249","volume":"8","author":"O Sagi","year":"2018","unstructured":"Sagi O, Rokach L (2018) Ensemble learning: A survey. WIREs Data Min & Knowl 8(4):e1249. https:\/\/doi.org\/10.1002\/widm.1249","journal-title":"WIREs Data Min & Knowl"},{"key":"19965_CR44","doi-asserted-by":"publisher","first-page":"150072","DOI":"10.1109\/ACCESS.2020.3016419","volume":"8","author":"M Alojail","year":"2020","unstructured":"Alojail M, Bhatia S (2020) A Novel Technique for Behavioral Analytics Using Ensemble Learning Algorithms in E-Commerce. IEEE Access 8:150072\u2013150080. https:\/\/doi.org\/10.1109\/ACCESS.2020.3016419","journal-title":"IEEE Access"},{"issue":"5","key":"19965_CR45","doi-asserted-by":"publisher","first-page":"7805","DOI":"10.1007\/s11042-020-09949-5","volume":"80","author":"S Forouzandeh","year":"2021","unstructured":"Forouzandeh S, Berahmand K, Rostami M (2021) Presentation of a recommender system with ensemble learning and graph embedding: a case on MovieLens. Multimed Tools Appl 80(5):7805\u20137832. https:\/\/doi.org\/10.1007\/s11042-020-09949-5","journal-title":"Multimed Tools Appl"},{"issue":"11","key":"19965_CR46","doi-asserted-by":"publisher","first-page":"651","DOI":"10.3390\/sym10110651","volume":"10","author":"M Yaman","year":"2018","unstructured":"Yaman M, Subasi A, Rattay F (2018) Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition. Symmetry 10(11):651. https:\/\/doi.org\/10.3390\/sym10110651","journal-title":"Symmetry"},{"issue":"3","key":"19965_CR47","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1007\/s12652-019-01451-7","volume":"11","author":"J PashaeiBarbin","year":"2020","unstructured":"PashaeiBarbin J, Yousefi S, Masoumi B (2020) Efficient service recommendation using ensemble learning in the internet of things (IoT). J Ambient Intell Human Comput 11(3):1339\u201350. https:\/\/doi.org\/10.1007\/s12652-019-01451-7","journal-title":"J Ambient Intell Human Comput"},{"key":"19965_CR48","doi-asserted-by":"publisher","unstructured":"Alrehili A, Albalawi K (2019) Sentiment analysis of customer reviews using ensemble method. In2019 International conference on computer and information sciences (ICCIS) (pp. 1-6). IEEE.6. https:\/\/doi.org\/10.1109\/ICCISci.2019.8716454.","DOI":"10.1109\/ICCISci.2019.8716454"},{"key":"19965_CR49","doi-asserted-by":"publisher","unstructured":"Sharma S, Srivastava S, Kumar A, Dangi A (2018) Multi-class sentiment analysis comparison using support vector machine (svm) and bagging technique-an ensemble method. In2018 International conference on smart computing and electronic enterprise (ICSCEE) (pp. 1-6). IEEE. https:\/\/doi.org\/10.1109\/ICSCEE.2018.8538397.","DOI":"10.1109\/ICSCEE.2018.8538397"},{"key":"19965_CR50","doi-asserted-by":"publisher","unstructured":"Hicham N, Karim S (2023) Machine Learning and Marketing Campaign: Innovative Approaches and Creative Techniques for Increasing Efficiency and Profit. InThe International Conference of Advanced Computing and Informatics (pp. 40-52). Cham: Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-031-36258-3_4.","DOI":"10.1007\/978-3-031-36258-3_4"},{"key":"19965_CR51","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1016\/j.procs.2018.05.109","volume":"132","author":"N Saleena","year":"2018","unstructured":"Saleena N (2018) An Ensemble Classification System for Twitter Sentiment Analysis. Procedia Comput Sci 132:937\u201346. https:\/\/doi.org\/10.1016\/j.procs.2018.05.109","journal-title":"Procedia Comput Sci"},{"key":"19965_CR52","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.eswa.2017.02.002","volume":"77","author":"O Araque","year":"2017","unstructured":"Araque O, Corcuera-Platas I, S\u00e1nchez-Rada JF, Iglesias CA (2017) Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Syst Appl 77:236\u2013246. https:\/\/doi.org\/10.1016\/j.eswa.2017.02.002","journal-title":"Expert Syst Appl"},{"key":"19965_CR53","doi-asserted-by":"publisher","unstructured":"Pasupulety U, Abdullah Anees A, Anmol S, Mohan BR (2019) \u201cPredicting Stock Prices using Ensemble Learning and Sentiment Analysis,\u201d in 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Sardinia, Italy: IEEE, pp. 215\u2013222. https:\/\/doi.org\/10.1109\/AIKE.2019.00045.","DOI":"10.1109\/AIKE.2019.00045"},{"issue":"1","key":"19965_CR54","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1016\/j.jksuci.2019.10.002","volume":"34","author":"RMK Saeed","year":"2022","unstructured":"Saeed RMK, Rady S, Gharib TF (2022) An ensemble approach for spam detection in Arabic opinion texts. J King Saud Univ - Comput Info Sci 34(1):1407\u20131416. https:\/\/doi.org\/10.1016\/j.jksuci.2019.10.002","journal-title":"J King Saud Univ - Comput Info Sci"},{"key":"19965_CR55","doi-asserted-by":"publisher","unstructured":"Oussous A, Lahcen AA, Belfkih S (2018) Improving sentiment analysis of moroccan tweets using ensemble learning. InBig Data, Cloud and Applications: Third International Conference, BDCA 2018, Kenitra, Morocco, April 4\u20135, 2018, Revised Selected Papers 3 (pp. 91-104). Springer International Publishing.https:\/\/doi.org\/10.1007\/978-3-319-96292-4_8.","DOI":"10.1007\/978-3-319-96292-4_8"},{"issue":"1","key":"19965_CR56","doi-asserted-by":"publisher","first-page":"60","DOI":"10.47540\/ijias.v2i1.432","volume":"2","author":"N Habbat","year":"2022","unstructured":"Habbat N, Anoun H, Hassouni L (2022) Sentiment Analysis and Topic Modeling on Arabic Twitter Data during Covid-19 Pandemic. IJIAS 2(1):60\u201367. https:\/\/doi.org\/10.47540\/ijias.v2i1.432","journal-title":"IJIAS"},{"issue":"4","key":"19965_CR57","doi-asserted-by":"publisher","first-page":"83","DOI":"10.3390\/a13040083","volume":"13","author":"G Haralabopoulos","year":"2020","unstructured":"Haralabopoulos G, Anagnostopoulos I, McAuley D (2020) Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content. Algorithms 13(4):83. https:\/\/doi.org\/10.3390\/a13040083","journal-title":"Algorithms"},{"issue":"10","key":"19965_CR58","doi-asserted-by":"publisher","first-page":"3707","DOI":"10.3390\/s22103707","volume":"22","author":"H Saleh","year":"2022","unstructured":"Saleh H, Mostafa S, Alharbi A, El-Sappagh S, Alkhalifah T (2022) Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis. Sensors 22(10):3707. https:\/\/doi.org\/10.3390\/s22103707","journal-title":"Sensors"},{"key":"19965_CR59","doi-asserted-by":"publisher","unstructured":"Deriu JM, Gonzenbach M, Uzdilli F, Lucchi A, De Luca V, Jaggi M (2016) Swisscheese at semeval-2016 task 4: Sentiment classification using an ensemble of convolutional neural networks with distant supervision. InProceedings of the 10th international workshop on semantic evaluation (SemEval-2016) (pp. 1124-1128). https:\/\/doi.org\/10.18653\/v1\/S16-1173.","DOI":"10.18653\/v1\/S16-1173"},{"key":"19965_CR60","doi-asserted-by":"publisher","unstructured":"Xu S, Liang H, Baldwin T (2016) Unimelb at semeval-2016 tasks 4a and 4b: An ensemble of neural networks and a word2vec based model for sentiment classification. InProceedings of the 10th international workshop on semantic evaluation (SemEval-2016) (pp. 183-189). https:\/\/doi.org\/10.18653\/v1\/S16-1027.","DOI":"10.18653\/v1\/S16-1027"},{"key":"19965_CR61","doi-asserted-by":"publisher","first-page":"966779","DOI":"10.3389\/fpubh.2022.966779","volume":"10","author":"S Albahli","year":"2022","unstructured":"Albahli S (2022) Twitter sentiment analysis: An Arabic text mining approach based on COVID-19. Front Public Health 10:966779. https:\/\/doi.org\/10.3389\/fpubh.2022.966779","journal-title":"Front Public Health"},{"key":"19965_CR62","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.dss.2013.08.002","volume":"57","author":"G Wang","year":"2014","unstructured":"Wang G, Sun J, Ma J, Xu K, Gu J (2014) Sentiment classification: The contribution of ensemble learning. Decis Support Syst 57:77\u201393. https:\/\/doi.org\/10.1016\/j.dss.2013.08.002","journal-title":"Decis Support Syst"},{"key":"19965_CR63","doi-asserted-by":"publisher","unstructured":"Kanakaraj M, Guddeti RM (2015) Performance analysis of Ensemble methods on Twitter sentiment analysis using NLP techniques. InProceedings of the 2015 IEEE 9th international conference on semantic computing (IEEE ICSC 2015) (pp. 169-170). IEEE. https:\/\/doi.org\/10.1109\/ICOSC.2015.7050801.","DOI":"10.1109\/ICOSC.2015.7050801"},{"key":"19965_CR64","doi-asserted-by":"publisher","unstructured":"Prusa J, Khoshgoftaar TM, Dittman DJ (2015) Using ensemble learners to improve classifier performance on tweet sentiment data. In2015 IEEE international conference on information reuse and integration (pp. 252-257). IEEE. https:\/\/doi.org\/10.1109\/IRI.2015.49.","DOI":"10.1109\/IRI.2015.49"},{"key":"19965_CR65","doi-asserted-by":"publisher","first-page":"103319","DOI":"10.1016\/j.engappai.2019.103319","volume":"87","author":"SE Roshan","year":"2020","unstructured":"Roshan SE, Asadi S (2020) Improvement of Bagging performance for classification of imbalanced datasets using evolutionary multi-objective optimization. Eng Appl Artif Intell 87:103319. https:\/\/doi.org\/10.1016\/j.engappai.2019.103319","journal-title":"Eng Appl Artif Intell"},{"key":"19965_CR66","doi-asserted-by":"publisher","unstructured":"Chujai P, Chomboon K, Teerarassamee P, Kerdprasop N, Kerdprasop K (2015) \u201cEnsemble Learning For Imbalanced Data Classification Problem,\u201d in The Proceedings of the 2nd International Conference on Industrial Application Engineering 2015, The Institute of Industrial Applications Engineers pp. 449\u2013456. https:\/\/doi.org\/10.12792\/iciae2015.079.","DOI":"10.12792\/iciae2015.079"},{"key":"19965_CR67","doi-asserted-by":"publisher","first-page":"103694","DOI":"10.1109\/ACCESS.2022.3210182","volume":"10","author":"KL Tan","year":"2022","unstructured":"Tan KL, Lee CP, Lim KM, Anbananthen KSM (2022) Sentiment Analysis With Ensemble Hybrid Deep Learning Model. IEEE Access 10:103694\u2013103704. https:\/\/doi.org\/10.1109\/ACCESS.2022.3210182","journal-title":"IEEE Access"},{"key":"19965_CR68","doi-asserted-by":"publisher","first-page":"200204","DOI":"10.1016\/j.iswa.2023.200204","volume":"18","author":"MA Muslim","year":"2023","unstructured":"Muslim MA et al (2023) New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning. Intell Syst Appl 18:200204. https:\/\/doi.org\/10.1016\/j.iswa.2023.200204","journal-title":"Intell Syst Appl"},{"key":"19965_CR69","doi-asserted-by":"publisher","unstructured":"Akhtyamova L, Ignatov A, Cardiff J (2017) \u201cA Large-Scale CNN Ensemble for Medication Safety Analysis,\u201d in Natural Language Processing and Information Systems, vol. 10260, F. Frasincar, A. Ittoo, L. M. Nguyen, and E. M\u00e9tais, Eds., in Lecture Notes in Computer Science, vol. 10260. , Cham: Springer International Publishing, pp. 247\u2013253. https:\/\/doi.org\/10.1007\/978-3-319-59569-6_29.","DOI":"10.1007\/978-3-319-59569-6_29"},{"key":"19965_CR70","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.procs.2018.10.466","volume":"142","author":"M Heikal","year":"2018","unstructured":"Heikal M, Torki M, El-Makky N (2018) Sentiment Analysis of Arabic Tweets using Deep Learning. Procedia Comput Sci 142:114\u2013122. https:\/\/doi.org\/10.1016\/j.procs.2018.10.466","journal-title":"Procedia Comput Sci"},{"key":"19965_CR71","doi-asserted-by":"publisher","unstructured":"Mohammadi A, Shaverizade A (2021) \u201cEnsemble deep learning for aspect-based sentiment analysis,\u201d IJNAA, vol. 12, no. Special Issue https:\/\/doi.org\/10.22075\/ijnaa.2021.4769.","DOI":"10.22075\/ijnaa.2021.4769"},{"key":"19965_CR72","doi-asserted-by":"publisher","unstructured":"RITM Laboratory, CED ENSEM Ecole Superieure de Technologie Hassan II University, Casablanca, Morocco, N. Habbat, H. Anoun, RITM Laboratory, CED ENSEM Ecole Superieure de Technologie Hassan II University, Casablanca, Morocco, L. Hassouni, and RITM Laboratory, CED ENSEM Ecole Superieure de Technologie Hassan II University, Casablanca, Morocco (2021) \u201cA Novel Hybrid Network for Arabic Sentiment Analysis using fine-tuned AraBERT model,\u201d ijeei, 13(4): 801\u2013812, https:\/\/doi.org\/10.15676\/ijeei.2021.13.4.3.","DOI":"10.15676\/ijeei.2021.13.4.3"},{"key":"19965_CR73","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: Synthetic Minority Over-sampling Technique. J Artificial Intell Res 16:321\u201357. https:\/\/doi.org\/10.1613\/jair.953","journal-title":"J Artificial Intell Res"},{"key":"19965_CR74","doi-asserted-by":"publisher","unstructured":"Antoun W, Baly F, Hajj H (2020) \u201cAraGPT2: Pre-Trained Transformer for Arabic Language Generation,\u201d https:\/\/doi.org\/10.48550\/ARXIV.2012.15520.","DOI":"10.48550\/ARXIV.2012.15520"},{"issue":"8","key":"19965_CR75","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I (2019) Language Models are Unsupervised Multitask Learners. OpenAI Blog 1(8):9","journal-title":"OpenAI Blog"},{"key":"19965_CR76","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.neucom.2016.02.006","volume":"193","author":"C Jian","year":"2016","unstructured":"Jian C, Gao J, Ao Y (2016) A new sampling method for classifying imbalanced data based on support vector machine ensemble. Neurocomputing 193:115\u2013122. https:\/\/doi.org\/10.1016\/j.neucom.2016.02.006","journal-title":"Neurocomputing"},{"key":"19965_CR77","doi-asserted-by":"publisher","unstructured":"Agustianto K, Destarianto P (2019) \u201cImbalance Data Handling using Neighborhood Cleaning Rule (NCL) Sampling Method for Precision Student Modeling,\u201d in 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE), Jember, Indonesia: IEEE, , pp. 86\u201389. https:\/\/doi.org\/10.1109\/ICOMITEE.2019.8921159.","DOI":"10.1109\/ICOMITEE.2019.8921159"},{"key":"19965_CR78","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) \u201cBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.\u201d arXiv, Accessed: Nov. 21, 2022. [Online]. Available: http:\/\/arxiv.org\/abs\/1810.04805"},{"key":"19965_CR79","unstructured":"\u201cbert-base-uncased \u00b7 Hugging Face.\u201d Accessed: Sep. 24, 2023. [Online]. Available: https:\/\/huggingface.co\/bert-base-uncased"},{"key":"19965_CR80","unstructured":"Liu et al Y (2019) \u201cRoBERTa: A Robustly Optimized BERT Pretraining Approach.\u201d arXiv, Accessed: Aug. 09, 2023. [Online]. Available: http:\/\/arxiv.org\/abs\/1907.11692"},{"key":"19965_CR81","unstructured":"Vaswani et al (2023) \u201cAttention Is All You Need.\u201d arXiv. Accessed: Aug. 09, 2023. [Online]. Available: http:\/\/arxiv.org\/abs\/1706.03762"},{"key":"19965_CR82","doi-asserted-by":"publisher","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky A (2020) Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Physica D 404:132306. https:\/\/doi.org\/10.1016\/j.physd.2019.132306","journal-title":"Physica D"},{"key":"19965_CR83","unstructured":"\u201cFichier:Long Short-Term Memory.svg \u2014 Wikip\u00e9dia.\u201d Accessed: Mar. 04, 2024. [Online]. Available: https:\/\/commons.wikimedia.org\/wiki\/File:Long_Short-Term_Memory.svg"},{"key":"19965_CR84","doi-asserted-by":"crossref","unstructured":"Cho K et al (2014) \u201cLearning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,\u201d arXiv:1406.1078 [cs, stat], Accessed: Aug. 27, 2021. [Online]. Available: http:\/\/arxiv.org\/abs\/1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"issue":"18","key":"19965_CR85","doi-asserted-by":"publisher","first-page":"8967","DOI":"10.3390\/app12188967","volume":"12","author":"H Saleh","year":"2022","unstructured":"Saleh H, Mostafa S, Gabralla LA, Aseeri AO, El-Sappagh S (2022) Enhanced Arabic Sentiment Analysis Using a Novel Stacking Ensemble of Hybrid and Deep Learning Models. Appl Sci 12(18):8967. https:\/\/doi.org\/10.3390\/app12188967","journal-title":"Appl Sci"},{"key":"19965_CR86","doi-asserted-by":"publisher","unstructured":"Feng W, Guan N, Li Y, Zhang X, Luo Z (2017) \u201cAudio visual speech recognition with multimodal recurrent neural networks,\u201d in 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA: IEEE, pp. 681\u2013688. https:\/\/doi.org\/10.1109\/IJCNN.2017.7965918.","DOI":"10.1109\/IJCNN.2017.7965918"},{"key":"19965_CR87","doi-asserted-by":"publisher","first-page":"51522","DOI":"10.1109\/ACCESS.2019.2909919","volume":"7","author":"G Xu","year":"2019","unstructured":"Xu G, Meng Y, Qiu X, Yu Z, Wu X (2019) Sentiment Analysis of Comment Texts Based on BiLSTM. IEEE Access 7:51522\u201351532. https:\/\/doi.org\/10.1109\/ACCESS.2019.2909919","journal-title":"IEEE Access"},{"issue":"1","key":"19965_CR88","doi-asserted-by":"publisher","first-page":"572","DOI":"10.11591\/ijai.v13.i1.pp572-580","volume":"13","author":"R Bani","year":"2024","unstructured":"Bani R, Amri S, Zenkouar L, Guennoun Z (2024) Toward accurate Amazigh part-of-speech tagging. IJ-AI 13(1):572. https:\/\/doi.org\/10.11591\/ijai.v13.i1.pp572-580","journal-title":"IJ-AI"},{"key":"19965_CR89","unstructured":"\u201cTwitter US Airline Sentiment.\u201d Accessed: Jan. 24, 2023. [Online]. Available: https:\/\/www.kaggle.com\/datasets\/crowdflower\/twitter-airline-sentiment"},{"key":"19965_CR90","unstructured":"Go A, Bhayani R, Huang L (2009) \u201cTwitter Sentiment Classification using Distant Supervision\u201d."},{"key":"19965_CR91","doi-asserted-by":"publisher","unstructured":"Elnagar A, Khalifa YS, Einea A (2018) Hotel Arabic-reviews dataset construction for sentiment analysis applications. Intelligent natural language processing: Trends and applications. 35-52. https:\/\/doi.org\/10.1007\/978-3-319-67056-0_3.","DOI":"10.1007\/978-3-319-67056-0_3"},{"key":"19965_CR92","doi-asserted-by":"crossref","unstructured":"Keung P, Lu Y, Szarvas G, Smith NA (2020) \u201cThe Multilingual Amazon Reviews Corpus,\u201d arXiv:2010.02573 [cs], Accessed: Aug. 22, 2021. [Online]. Available: http:\/\/arxiv.org\/abs\/2010.02573","DOI":"10.18653\/v1\/2020.emnlp-main.369"},{"key":"19965_CR93","unstructured":"allocine Datasets at Hugging Face. Available: https:\/\/huggingface.co\/datasets\/allocine. Accessed 26 Sep 2023"},{"issue":"2","key":"19965_CR94","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","volume":"405","author":"BW Matthews","year":"1975","unstructured":"Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA) - Protein Structure 405(2):442\u201351","journal-title":"Biochimica et Biophysica Acta (BBA) - Protein Structure"},{"key":"19965_CR95","doi-asserted-by":"publisher","first-page":"106999","DOI":"10.1016\/j.engappai.2023.106999","volume":"126","author":"N Habbat","year":"2023","unstructured":"Habbat N, Nouri H, Anoun H, Hassouni L (2023) Sentiment analysis of imbalanced datasets using BERT and ensemble stacking for deep learning. Eng Appl Artif Intell 126:106999. https:\/\/doi.org\/10.1016\/j.engappai.2023.106999","journal-title":"Eng Appl Artif Intell"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19965-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19965-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19965-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T12:57:06Z","timestamp":1732021026000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19965-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,10]]},"references-count":95,"journal-issue":{"issue":"39","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["19965"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19965-4","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,10]]},"assertion":[{"value":"12 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 August 2024","order":4,"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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}]}}