{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T22:21:41Z","timestamp":1761949301020,"version":"build-2065373602"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s40747-025-02086-2","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T10:14:12Z","timestamp":1760091252000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GPT-based lifelong learning and ANFIS-driven reply memory ratio prediction for aspect-based sentiment analysis"],"prefix":"10.1007","volume":"11","author":[{"given":"Huang","family":"Huang","sequence":"first","affiliation":[]},{"given":"Xiaohong","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Mumtaz Begum","family":"Mustafa","sequence":"additional","affiliation":[]},{"given":"Qiyuan","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Adeleh","family":"Asemi","sequence":"additional","affiliation":[]},{"given":"Asefeh","family":"Asemi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,10]]},"reference":[{"key":"2086_CR1","doi-asserted-by":"publisher","first-page":"90367","DOI":"10.1109\/ACCESS.2023.3307308","volume":"11","author":"H Huang","year":"2023","unstructured":"Huang H, Zavareh AA, Mustafa MB (2023) Sentiment analysis in e-commerce platforms: a review of current techniques and future directions. IEEE Access 11:90367\u201390382","journal-title":"IEEE Access"},{"key":"2086_CR2","unstructured":"Lopez-Paz D, Ranzato MA (2017) Gradient episodic memory for continual learning. Adv Neural Inf Process Syst, 30"},{"key":"2086_CR3","doi-asserted-by":"crossref","unstructured":"Ding X, Zhou J, Dou L, Chen Q, Wu Y, Chen C, He L (2024) Boosting large Language models with continual learning for aspect-based sentiment analysis. ArXiv Preprint arXiv:240505496","DOI":"10.18653\/v1\/2024.findings-emnlp.252"},{"key":"2086_CR4","doi-asserted-by":"crossref","unstructured":"Ke Z, Liu B, Wang H, Shu L (2021) Continual learning with knowledge transfer for sentiment classification. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14\u201318, 2020, Proceedings, Part III (pp. 683\u2013698). Springer International Publishing","DOI":"10.1007\/978-3-030-67664-3_41"},{"key":"2086_CR5","doi-asserted-by":"crossref","unstructured":"Zhang Z, Wang J, Nie K, Wang X, Liu J (2024), September Lifelong Sentiment Classification Based on Adaptive Parameter Updating. In International Conference on Artificial Neural Networks (pp. 262\u2013276). Cham: Springer Nature Switzerland","DOI":"10.1007\/978-3-031-72350-6_18"},{"key":"2086_CR6","doi-asserted-by":"publisher","first-page":"107540","DOI":"10.1016\/j.knosys.2021.107540","volume":"261","author":"A-S Mohammad","year":"2023","unstructured":"Mohammad A-S, Hammad MM, Sa\u2019ad A, Saja A-T, Cambria E (2023) Gated recurrent unit with multilingual universal sentence encoder for Arabic aspect-based sentiment analysis. Knowl Based Syst 261:107540","journal-title":"Knowl Based Syst"},{"key":"2086_CR7","doi-asserted-by":"crossref","unstructured":"Wang W, Pan SJ, Dahlmeier D, Xiao X (2016) Recursive Neural Conditional Random Fields for Aspect-Based Sentiment Analysis. Proc. 2016 Conf. Empirical Methods in Natural Language Processing, pp. 616\u2013626","DOI":"10.18653\/v1\/D16-1059"},{"key":"2086_CR8","doi-asserted-by":"crossref","unstructured":"Li X, Lam W (2017) Deep multi-task learning for aspect term extraction with memory interaction. Proc. 2017 Conf. Empirical Methods in Natural Language Processing, pp. 2886\u20132892","DOI":"10.18653\/v1\/D17-1310"},{"key":"2086_CR9","doi-asserted-by":"crossref","unstructured":"Ma D, Li S, Wang H (2018) Joint learning for targeted sentiment analysis. Proc. 2018 Conf. Empirical Methods in Natural Language Processing, pp. 4737\u20134742","DOI":"10.18653\/v1\/D18-1504"},{"key":"2086_CR10","doi-asserted-by":"crossref","unstructured":"Seker A, Bandel E, Bareket D, Brusilovsky I, Greenfeld R, Tsarfaty R (2022), May AlephBERT: language model pre-training and evaluation from sub-word to sentence level. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 46\u201356)","DOI":"10.18653\/v1\/2022.acl-long.4"},{"key":"2086_CR11","doi-asserted-by":"publisher","first-page":"100551","DOI":"10.1109\/ACCESS.2020.2997675","volume":"8","author":"J Su","year":"2020","unstructured":"Su J, Yu S, Luo D (2020) Enhancing aspect-based sentiment analysis with capsule network. IEEE Access 8:100551\u2013100561","journal-title":"IEEE Access"},{"key":"2086_CR12","unstructured":"Jiang B (2024) All in One: An empirical study of GPT for few-shot aspect-based sentiment anlaysis. arXiv preprint arXiv:2404.06063"},{"key":"2086_CR13","unstructured":"Rusu AA, Rabinowitz NC, Desjardins G, Soyer H, Kirkpatrick J, Kavukcuoglu K, Hadsell R (2016) Progressive neural networks. arXiv preprint arXiv:1606.04673."},{"key":"2086_CR14","unstructured":"Yoon J, Yang E, Lee J, Hwang SJ (2017) Lifelong learning with dynamically expandable networks. ArXiv Preprint arXiv:170801547"},{"key":"2086_CR15","doi-asserted-by":"crossref","unstructured":"Wickramasinghe B, Saha G, Roy K (2023) Continual learning: A review of techniques, challenges and future directions. IEEE Transactions on Artificial Intelligence","DOI":"10.1109\/TAI.2023.3339091"},{"key":"2086_CR16","doi-asserted-by":"crossref","unstructured":"Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, Hadsell R (2017) Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences 114(13):3521\u20133526.","DOI":"10.1073\/pnas.1611835114"},{"key":"2086_CR17","doi-asserted-by":"crossref","unstructured":"Rebuffi SA, Kolesnikov A, Sperl G, Lampert CH (2017) icarl: Incremental classifier and representation learning. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (pp. 2001\u20132010)","DOI":"10.1109\/CVPR.2017.587"},{"issue":"12","key":"2086_CR18","doi-asserted-by":"publisher","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","volume":"40","author":"Z Li","year":"2017","unstructured":"Li Z, Hoiem D (2017) Learning without forgetting. IEEE Trans Pattern Anal Mach Intell 40(12):2935\u20132947","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"2086_CR19","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1080\/09540099550039318","volume":"7","author":"A Robins","year":"1995","unstructured":"Robins A (1995) Catastrophic forgetting, rehearsal and pseudorehearsal. Connection Sci 7(2):123\u2013146","journal-title":"Connection Sci"},{"key":"2086_CR20","doi-asserted-by":"crossref","unstructured":"Pellegrini L, Graffieti G, Lomonaco V, Maltoni D (2020) Latent replay for realtime continual learning. In 2020 ieee\/rsj international conference on intelligent robots and systems (iros) (pp. 10203\u201310209)","DOI":"10.1109\/IROS45743.2020.9341460"},{"key":"2086_CR21","unstructured":"Shin H, Lee JK, Kim J, Kim J (2017) Continual learning with deep generative replay. Adv Neural Inf Process Syst, 30"},{"key":"2086_CR22","unstructured":"an de Ven GM, Tolias AS (2018) Generative replay with feedback connections as a general strategy for continual learning. ArXiv Preprint arXiv:180910635"},{"key":"2086_CR23","doi-asserted-by":"publisher","unstructured":"Ren Y, Zhang Y, Ji D (2018) Lifelong learning memory networks for aspect sentiment classification. In proceedings of the 2018 conference on empirical methods in natural language processing (EMNLP) (pp. 2514\u20132524). https:\/\/doi.org\/10.18653\/v1\/D18-1272","DOI":"10.18653\/v1\/D18-1272"},{"key":"2086_CR24","doi-asserted-by":"publisher","unstructured":"Ke P, Xu H, He Y, Zhang B (2021) Adapting BERT for continual learning of a sequence of aspect sentiment classification tasks. In: Findings of the Association for Computational Linguistics: EMNLP 2021 (pp. 4521\u20134532). https:\/\/doi.org\/10.18653\/v1\/2021.findings-emnlp.388","DOI":"10.18653\/v1\/2021.findings-emnlp.388"},{"key":"2086_CR25","doi-asserted-by":"publisher","unstructured":"Liu L, Zhang Y, Liu Q (2021) CLASSIC: Continual and contrastive learning for aspect sentiment classification. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL) (pp. 3720\u20133730). https:\/\/doi.org\/10.18653\/v1\/2021.acl-main.293","DOI":"10.18653\/v1\/2021.acl-main.293"},{"key":"2086_CR26","unstructured":"Chen M, Shen Z, Wang X, Wu Y, Zhang Y (2021) Federated contrastive learning with feature-based distillation for human activity recognition. arXiv preprint arXiv:2109.06895. https:\/\/arxiv.org\/abs\/2109.06895"},{"key":"2086_CR27","unstructured":"Zhang T, Wu H, Li J, Zhou J, Lin Z (2022) Deep contrastive representation learning with self-distillation. arXiv preprint arXiv:2201.12345. https:\/\/arxiv.org\/abs\/2201.12345"},{"key":"2086_CR28","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.procs.2023.12.030","volume":"229","author":"S Chumakov","year":"2023","unstructured":"Chumakov S, Kovantsev A, Surikov A (2023) Generative approach to aspect based sentiment analysis with GPT language models. Procedia Comput Sci 229:284\u2013293","journal-title":"Procedia Comput Sci"},{"key":"2086_CR29","unstructured":"Simmering PF, Huoviala P (2023) Large language models for aspect-based sentiment analysis. arXiv preprint arXiv:2310.18025"},{"key":"2086_CR30","doi-asserted-by":"crossref","unstructured":"Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, Milan K, Quan J et al (2017) Tiago Ramalho, Agnieszka Grabska-Barwinska,. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences, 114(13):3521\u20133526","DOI":"10.1073\/pnas.1611835114"},{"key":"2086_CR31","doi-asserted-by":"publisher","first-page":"126051","DOI":"10.1109\/ACCESS.2020.3008874","volume":"8","author":"H Wang","year":"2020","unstructured":"Wang H, Hou M, Li F, Zhang Y (2020) Chinese implicit sentiment analysis based on hierarchical knowledge enhancement and multi-pooling. IEEE Access 8:126051\u2013126065","journal-title":"IEEE Access"},{"key":"2086_CR32","unstructured":"Zhang J, Fu Y, Peng Z, Yao D, He K (2024) Core: mitigating catastrophic forgetting in continual learning through cognitive replay. ArXiv preprintarXiv:240201348"},{"key":"2086_CR33","unstructured":"Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., \u2026 Liu, P. J.(2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research 21(140):1\u201367."},{"key":"2086_CR34","doi-asserted-by":"crossref","unstructured":"Reimers N, Gurevych I (2019) Sentence-BERT: Sentence embeddings using Siamese BERT-networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 3982\u20133992","DOI":"10.18653\/v1\/D19-1410"},{"key":"2086_CR35","doi-asserted-by":"publisher","first-page":"86","DOI":"10.18653\/v1\/P16-1009","volume":"1: Long Papers","author":"R Sennrich","year":"2016","unstructured":"Sennrich R, Haddow B, Birch A (2016) Improving neural machine translation models with monolingual data. Proc 54th Annual Meeting Association Comput Linguistics 1: Long Papers:86\u201396","journal-title":"Proc 54th Annual Meeting Association Comput Linguistics"},{"key":"2086_CR36","doi-asserted-by":"crossref","unstructured":"Wei J, Zou K (2019) EDA: easy data augmentation techniques for boosting performance on text classification tasks. arXiv preprint arXiv:1901.11196","DOI":"10.18653\/v1\/D19-1670"},{"key":"2086_CR37","first-page":"6256","volume":"33","author":"Q Xie","year":"2020","unstructured":"Xie Q, Dai Z, Hovy E, Luong M-T, Le QV (2020) Unsupervised data augmentation for consistency training. Adv Neural Inform Process Syst (NeurIPS) 33:6256\u20136268","journal-title":"Adv Neural Inform Process Syst (NeurIPS)"},{"issue":"1","key":"2086_CR38","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1186\/s40537-024-00965-y","volume":"11","author":"A Asemi","year":"2024","unstructured":"Asemi A, Asemi A, Ko A (2024) A model for investment type recommender system based on the potential investors based on investors and experts feedback using ANFIS and MNN. J Big Data 11(1):128","journal-title":"J Big Data"},{"key":"2086_CR39","doi-asserted-by":"crossref","unstructured":"Asemi A, Asemi A, Ko A (2023) Unveiling the impact of managerial traits on investor decision prediction: ANFIS approach. Soft Comput, 1\u201321","DOI":"10.1007\/s00500-023-08102-2"},{"key":"2086_CR40","doi-asserted-by":"crossref","unstructured":"Kumari, S., Agarwal, S., Kumar, M., Sharma, P., Kumar, A., Hashem, A., \u2026 Garg, M.C. (2025). An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach. Scientific Reports 15(1):2979.","DOI":"10.1038\/s41598-025-87274-3"},{"issue":"1","key":"2086_CR41","doi-asserted-by":"publisher","first-page":"5196","DOI":"10.1038\/s41598-025-88316-6","volume":"15","author":"L Kolsi","year":"2025","unstructured":"Kolsi L, Behroyan I, Darweesh MS, Alshammari BM, Armaghani T, Babanezhad M (2025) ANFIS algorithm for mapping computational data of water reservoir homogenization with air bubble flows. Sci Rep 15(1):5196","journal-title":"Sci Rep"},{"issue":"1","key":"2086_CR42","doi-asserted-by":"publisher","first-page":"8550","DOI":"10.1038\/s41598-025-92821-z","volume":"15","author":"AA Shaier","year":"2025","unstructured":"Shaier AA, Flah A, Kraiem H, Enany MA, Elymany MM (2025) Novel technique for precise derating torque of induction motors using ANFIS. Sci Rep 15(1):8550","journal-title":"Sci Rep"},{"key":"2086_CR43","doi-asserted-by":"publisher","first-page":"154290","DOI":"10.1109\/ACCESS.2019.2946594","volume":"7","author":"Z Gao","year":"2019","unstructured":"Gao Z, Feng A, Song X, Wu X (2019) Target-dependent sentiment classification with BERT. Ieee Access 7:154290\u2013154299","journal-title":"Ieee Access"},{"key":"2086_CR44","unstructured":"Hoang M, Bihorac OA, Rouces J (2019) Aspect-based sentiment analysis using bert. In: Proceedings of the 22nd nordic conference on computational linguistics (pp. 187\u2013196)"},{"key":"2086_CR45","unstructured":"Chen T, Goodfellow I, Shlens J (2015) Net2net: Accelerating learning via knowledge transfer. arXiv preprint arXiv:1511.05641"},{"key":"2086_CR46","doi-asserted-by":"crossref","unstructured":"Mallya A, Lazebnik S (2018) Packnet: adding multiple tasks to a single network by iterative pruning. In: Proceedings of the ieee conference on computer vision and pattern recognition (pp. 7765\u20137773)","DOI":"10.1109\/CVPR.2018.00810"},{"key":"2086_CR47","unstructured":"Fernando C, Banarse D, Blundell C, Zwols Y, Ha D, Rusu AA, Wierstra D (2017) Pathnet: evolution channels gradient descent in super neural networks. arXiv preprint arXiv:1701.08734"},{"key":"2086_CR48","unstructured":"Zenke F, Poole B, Ganguli S (2017) Continual learning through synaptic intelligence. In: International conference on machine learning (pp. 3987\u20133995)"},{"key":"2086_CR49","unstructured":"Chaudhry A, Ranzato M, Rohrbach M, Elhoseiny M (2018) Efficient lifelong learning with a-gem. ArXiv Preprint arXiv:181200420"},{"key":"2086_CR50","unstructured":"Li H, Wu J, Braverman V (2023) Fixed design analysis of regularization-based continual learning. In: Conference on lifelong learning agents (pp. 513\u2013533)"},{"key":"2086_CR51","unstructured":"Ahn H, Cha S, Lee D, Moon T (2019) Uncertainty-based continual learning with adaptive regularization. Adv Neural Inf Process Syst, 32"},{"key":"2086_CR52","unstructured":"Nichol A, Achiam J, Schulman J (2018) On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999."},{"key":"2086_CR53","unstructured":"Raghu A, Raghu M et al (2020) Rapid learning or feature reuse? Towards understanding the effectiveness of MAML. International Conference on Learning Representations (ICLR)"},{"key":"2086_CR54","first-page":"1","volume-title":"Cel: A continual learning model for disease outbreak prediction by leveraging domain adaptation via elastic weight consolidation","author":"S Aslam","year":"2025","unstructured":"Aslam S, Rasool A, Li X, Wu H (2025) Cel: A continual learning model for disease outbreak prediction by leveraging domain adaptation via elastic weight consolidation. Computational Life Sciences, Interdisciplinary Sciences, pp 1\u201319"},{"key":"2086_CR55","doi-asserted-by":"publisher","unstructured":"Ho S, Liu M, Gao S, Gao L (2024) Learning to learn for few-shot continual active learning. Artif Intell Rev 57(10). https:\/\/doi.org\/10.1007\/s10462-024-10924-x","DOI":"10.1007\/s10462-024-10924-x"},{"key":"2086_CR56","unstructured":"Gajic A, Vhaduri S (2025) A comprehensive survey of challenges and opportunities of few-shot learning across multiple domains. arXiv preprint arXiv:2504.04017."},{"issue":"3","key":"2086_CR57","doi-asserted-by":"publisher","first-page":"56","DOI":"10.3390\/ai6030056","volume":"6","author":"A Rasool","year":"2025","unstructured":"Rasool A, Shahzad MI, Aslam H, Chan V, Arshad MA (2025) Emotion-aware embedding fusion in large Language models (Flan-T5, Llama 2, DeepSeek-R1, and ChatGPT 4) for intelligent response generation. AI 6(3):56","journal-title":"AI"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02086-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-02086-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02086-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T22:15:12Z","timestamp":1761948912000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-02086-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,10]]},"references-count":57,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["2086"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-02086-2","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2025,10,10]]},"assertion":[{"value":"10 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 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":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"463"}}