{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T17:08:18Z","timestamp":1774026498084,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"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":["J Supercomput"],"DOI":"10.1007\/s11227-025-07965-6","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T09:00:54Z","timestamp":1760691654000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Sentiment analysis of movie reviews based on quantum convolutional neural networks"],"prefix":"10.1007","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5570-4058","authenticated-orcid":false,"given":"Nour El Houda","family":"Ouamane","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7313-5406","authenticated-orcid":false,"given":"Hacene","family":"Belhadef","sequence":"additional","affiliation":[]},{"given":"Mohammed","family":"Haddad","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"7965_CR1","doi-asserted-by":"crossref","unstructured":"Goniwada SR (2023) Sentiment analysis. Introduction to Datafication: Implement Datafication Using AI and ML Algorithms. Apress, Berkeley, CA, pp 165\u2013184","DOI":"10.1007\/978-1-4842-9496-3_6"},{"key":"7965_CR2","doi-asserted-by":"crossref","unstructured":"Kirilenko AP, Wang L, Stepchenkova SO (2022) Sentiment analysis: gaging opinions of large groups. In: Applied data science in tourism: interdisciplinary approaches, methodologies, and applications. Springer, Cham, pp 363\u2013374","DOI":"10.1007\/978-3-030-88389-8_17"},{"issue":"1","key":"7965_CR3","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1146\/annurev-linguistics-011415-040518","volume":"2","author":"M Taboada","year":"2016","unstructured":"Taboada M (2016) Sentiment analysis: an overview from linguistics. Annu Rev Linguist 2(1):325\u2013347","journal-title":"Annu Rev Linguist"},{"key":"7965_CR4","doi-asserted-by":"crossref","unstructured":"Mohammad SM (2016) Sentiment analysis: detecting valence, emotions, and other affectual states from text. In: Emotion measurement. Woodhead Publishing, pp 201\u2013237","DOI":"10.1016\/B978-0-08-100508-8.00009-6"},{"key":"7965_CR5","doi-asserted-by":"crossref","unstructured":"Liu B (2020) Sentiment analysis: mining opinions, sentiments, and emotions. Cambridge University Press","DOI":"10.1017\/9781108639286"},{"issue":"2","key":"7965_CR6","doi-asserted-by":"publisher","first-page":"97","DOI":"10.4995\/jarte.2023.19306","volume":"4","author":"F Hassan","year":"2023","unstructured":"Hassan F, Qureshi NA, Khan MZ, Khan MA, Soomro AS, Imroz A, Marri HB (2023) Performance evolution for sentiment classification using machine learning algorithm. J Appl Res Technol Eng 4(2):97\u2013110","journal-title":"J Appl Res Technol Eng"},{"key":"7965_CR7","doi-asserted-by":"crossref","unstructured":"Singh J, Sharma G (2023) Sentiment analysis study of human thoughts using machine learning techniques. In: 2023 International Conference on Disruptive Technologies (ICDT). IEEE, pp 776\u2013785","DOI":"10.1109\/ICDT57929.2023.10150917"},{"issue":"14","key":"7965_CR8","doi-asserted-by":"publisher","first-page":"21353","DOI":"10.1007\/s11042-022-13801-3","volume":"82","author":"E Arkin","year":"2023","unstructured":"Arkin E, Yadikar N, Xu X, Aysa A, Ubul K (2023) A survey: object detection methods from CNN to transformer. Multimedia Tools Appl 82(14):21353\u201321383","journal-title":"Multimedia Tools Appl"},{"issue":"9","key":"7965_CR9","doi-asserted-by":"publisher","first-page":"5521","DOI":"10.3390\/app13095521","volume":"13","author":"J Maur\u00edcio","year":"2023","unstructured":"Maur\u00edcio J, Domingues I, Bernardino J (2023) Comparing vision transformers and convolutional neural networks for image classification: a literature review. Appl Sci 13(9):5521","journal-title":"Appl Sci"},{"key":"7965_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109228","volume":"136","author":"F Yuan","year":"2023","unstructured":"Yuan F, Zhang Z, Fang Z (2023) An effective CNN and Transformer complementary network for medical image segmentation. Pattern Recogn 136:109228","journal-title":"Pattern Recogn"},{"issue":"1","key":"7965_CR11","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/s00530-022-00973-z","volume":"29","author":"Z Chen","year":"2023","unstructured":"Chen Z, Chen J, Ding G, Huang H (2023) A lightweight CNN-based algorithm and implementation on embedded system for real-time face recognition. Multimedia Syst 29(1):129\u2013138","journal-title":"Multimedia Syst"},{"issue":"5","key":"7965_CR12","doi-asserted-by":"publisher","first-page":"2694","DOI":"10.3390\/app12052694","volume":"12","author":"L Khan","year":"2022","unstructured":"Khan L, Amjad A, Afaq KM, Chang HT (2022) Deep sentiment analysis using CNN-LSTM architecture of English and Roman Urdu text shared in social media. Appl Sci 12(5):2694","journal-title":"Appl Sci"},{"key":"7965_CR13","doi-asserted-by":"crossref","unstructured":"Ouamane NEH, Belhadef H (2023) Proposed model for QCNN-based sentimental short sentences classification. In: International Conference of Reliable Information and Communication Technology. Springer, Cham, pp 214\u2013223","DOI":"10.1007\/978-3-031-59707-7_19"},{"key":"7965_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110307","volume":"141","author":"E Ovalle-Magallanes","year":"2023","unstructured":"Ovalle-Magallanes E, Alvarado-Carrillo DE, Avina-Cervantes JG, Cruz-Aceves I, Ruiz-Pinales J (2023) Quantum angle encoding with learnable rotation applied to quantum\u2013classical convolutional neural networks. Appl Soft Comput 141:110307","journal-title":"Appl Soft Comput"},{"issue":"05","key":"7965_CR15","doi-asserted-by":"crossref","first-page":"165","DOI":"10.47392\/irjash.2023.032","volume":"5","author":"C Aishwarya","year":"2023","unstructured":"Aishwarya C, Venkatesan M, Prabhavathy P (2023) Research oriented reviewing of quantum machine learning. Int Res J Adv Sci Hub 5(05):165\u2013177","journal-title":"Int Res J Adv Sci Hub"},{"issue":"12","key":"7965_CR16","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1038\/s41567-019-0648-8","volume":"15","author":"I Cong","year":"2019","unstructured":"Cong I, Choi S, Lukin MD (2019) Quantum convolutional neural networks. Nat Phys 15(12):1273\u20131278","journal-title":"Nat Phys"},{"issue":"1","key":"7965_CR17","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s42484-021-00061-x","volume":"4","author":"T Hur","year":"2022","unstructured":"Hur T, Kim L, Park DK (2022) Quantum convolutional neural network for classical data classification. Quant Mach Intell 4(1):3","journal-title":"Quant Mach Intell"},{"key":"7965_CR18","unstructured":"Meedinti GN, Srirekha KS, Delhibabu R (2023) A quantum convolutional neural network approach for object detection and classification. arXiv preprint arXiv:2307.08204"},{"key":"7965_CR19","doi-asserted-by":"crossref","unstructured":"Dong Y, Fu Y, Liu H, Che X, Sun L, Luo Y (2023) An improved hybrid quantum-classical convolutional neural network for multi-class brain tumor MRI classification. J Appl Phys 133(6)","DOI":"10.1063\/5.0138021"},{"key":"7965_CR20","doi-asserted-by":"crossref","unstructured":"Zhang Y, Song D, Li X, Zhang P (2018) Unsupervised sentiment analysis of twitter posts using density matrix representation. In: Advances in Information Retrieval: 40th European Conference on IR Research, ECIR 2018, Grenoble, France, March 26-29, 2018, Proceedings 40. Springer, pp 316\u2013329","DOI":"10.1007\/978-3-319-76941-7_24"},{"key":"7965_CR21","doi-asserted-by":"crossref","unstructured":"Zhang Y, Li Q, Song D, Zhang P, Wang P (2019) Quantum-inspired interactive networks for conversational sentiment analysis","DOI":"10.24963\/ijcai.2019\/755"},{"key":"7965_CR22","unstructured":"Zhang P, Zhang J, Ma X, Rao S, Tian G, Wang J (2021) TextTN: probabilistic encoding of language on tensor network"},{"key":"7965_CR23","doi-asserted-by":"crossref","unstructured":"Day W, Chen HS, Sun MT (2023) QNet: a quantum-native sequence encoder architecture. In: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), Vol. 1. IEEE, pp 246\u2013255","DOI":"10.1109\/QCE57702.2023.00035"},{"key":"7965_CR24","doi-asserted-by":"crossref","unstructured":"Dave K, Innan N, Behera BK, Mumtaz Z, Al-Kuwari S, Farouk A (2024) SentiQNF: A Novel Approach to Sentiment Analysis Using Quantum Algorithms and Neuro-Fuzzy Systems. arXiv preprint arXiv:2412.12731","DOI":"10.1109\/TCSS.2025.3588779"},{"issue":"11","key":"7965_CR25","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.101.110501","volume":"101","author":"G Vidal","year":"2008","unstructured":"Vidal G (2008) Class of quantum many-body states that can be efficiently simulated. Phys Rev Lett 101(11):110501","journal-title":"Phys Rev Lett"},{"key":"7965_CR26","doi-asserted-by":"crossref","unstructured":"El-Din DM (2016) Enhancement bag-of-words model for solving the challenges of sentiment analysis. Int J Adv Comput Sci Appl 7(1)","DOI":"10.14569\/IJACSA.2016.070134"},{"key":"7965_CR27","doi-asserted-by":"crossref","unstructured":"Chiny M, Chihab M, Bencharef O, Chihab Y (2021) LSTM, VADER and TF-IDF based hybrid sentiment analysis model. Int J Adv Comput Sci Appl 12(7)","DOI":"10.14569\/IJACSA.2021.0120730"},{"issue":"23","key":"7965_CR28","doi-asserted-by":"publisher","first-page":"11255","DOI":"10.3390\/app112311255","volume":"11","author":"M Kamyab","year":"2021","unstructured":"Kamyab M, Liu G, Adjeisah M (2021) Attention-based CNN and Bi-LSTM model based on TF-IDF and glove word embedding for sentiment analysis. Appl Sci 11(23):11255","journal-title":"Appl Sci"},{"key":"7965_CR29","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1016\/j.procs.2020.03.416","volume":"167","author":"AK Sharma","year":"2020","unstructured":"Sharma AK, Chaurasia S, Srivastava DK (2020) Sentimental short sentences classification by using CNN deep learning model with fine tuned Word2Vec. Procedia Comput Sci 167:1139\u20131147","journal-title":"Procedia Comput Sci"},{"key":"7965_CR30","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/978-3-030-83098-4","volume-title":"Machine learning with quantum computers","author":"M Schuld","year":"2021","unstructured":"Schuld M, Petruccione F (2021) Machine learning with quantum computers, vol 676. Springer, Berlin, pp 163\u2013169"},{"issue":"9","key":"7965_CR31","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1038\/s42254-021-00348-9","volume":"3","author":"M Cerezo","year":"2021","unstructured":"Cerezo M, Arrasmith A, Babbush R, Benjamin SC, Endo S, Fujii K, Coles PJ (2021) Variational quantum algorithms. Nat Rev Phys 3(9):625\u2013644","journal-title":"Nat Rev Phys"},{"issue":"7671","key":"7965_CR32","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/nature23474","volume":"549","author":"J Biamonte","year":"2017","unstructured":"Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S (2017) Quantum machine learning. Nature 549(7671):195\u2013202","journal-title":"Nature"},{"issue":"1","key":"7965_CR33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41534-018-0113-z","volume":"5","author":"F Tacchino","year":"2019","unstructured":"Tacchino F, Macchiavello C, Gerace D, Bajoni D (2019) An artificial neuron implemented on an actual quantum processor. npj Quant Inf 5(1):1\u20138","journal-title":"npj Quant Inf"},{"issue":"4","key":"7965_CR34","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.122.040504","volume":"122","author":"M Schuld","year":"2019","unstructured":"Schuld M, Killoran N (2019) Quantum machine learning in feature Hilbert spaces. Phys Rev Lett 122(4):040504","journal-title":"Phys Rev Lett"},{"issue":"7747","key":"7965_CR35","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1038\/s41586-019-0980-2","volume":"567","author":"V Havl\u00ed\u010dek","year":"2019","unstructured":"Havl\u00ed\u010dek V, C\u00f3rcoles AD, Temme K, Harrow AW, Kandala A, Chow JM, Gambetta JM (2019) Supervised learning with quantum-enhanced feature spaces. Nature 567(7747):209\u2013212","journal-title":"Nature"},{"key":"7965_CR36","doi-asserted-by":"publisher","first-page":"79","DOI":"10.22331\/q-2018-08-06-79","volume":"2","author":"J Preskill","year":"2018","unstructured":"Preskill J (2018) Quantum computing in the NISQ era and beyond. Quantum 2:79","journal-title":"Quantum"},{"key":"7965_CR37","doi-asserted-by":"crossref","unstructured":"Belhadef H, Benchiheb H, Lebdjiri L (2023) Exploring the capabilities and limitations of vqc and qsvc for sentiment analysis on real-world and synthetic datasets. In: European Conference on Advances in Databases and Information Systems. Springer, Cham, pp 415\u2013424","DOI":"10.1007\/978-3-031-42941-5_36"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07965-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-07965-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-07965-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T09:01:01Z","timestamp":1760691661000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-07965-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,17]]},"references-count":37,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["7965"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-07965-6","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,17]]},"assertion":[{"value":"14 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2025","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This research was done according to ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}}],"article-number":"1473"}}