{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T22:19:29Z","timestamp":1793744369297,"version":"4.6.1"},"reference-count":76,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T00:00:00Z","timestamp":1785801600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Data &amp; Knowledge Engineering"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.datak.2026.102634","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T15:45:59Z","timestamp":1785858359000},"page":"102634","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Hierarchical aspect-based sentiment analysis Using FinRoBERTa"],"prefix":"10.1016","volume":"165","author":[{"given":"Stefan","family":"Straleger","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8031-758X","authenticated-orcid":false,"given":"Flavius","family":"Frasincar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.datak.2026.102634_b1","series-title":"Stock returns after major price shocks: The impact of information","first-page":"635","author":"Savor","year":"2012"},{"issue":"1","key":"10.1016\/j.datak.2026.102634_b2","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1111\/jofi.12302","article-title":"News trading and speed","volume":"71","author":"Foucault","year":"2016","journal-title":"J. Financ."},{"issue":"2","key":"10.1016\/j.datak.2026.102634_b3","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.jfineco.2016.01.008","article-title":"Sentiments, financial markets, and macroeconomic fluctuations","volume":"120","author":"Benhabib","year":"2016","journal-title":"J. Financ. Econ."},{"key":"10.1016\/j.datak.2026.102634_b4","series-title":"2016 International Conference on Signal Processing, Communication, Power and Embedded System","first-page":"1345","article-title":"Sentiment analysis of Twitter data for predicting stock market movements","author":"Pagolu","year":"2016"},{"key":"10.1016\/j.datak.2026.102634_b5","series-title":"Survey on aspect-level sentiment analysis","first-page":"813","author":"Schouten","year":"2016"},{"key":"10.1016\/j.datak.2026.102634_b6","series-title":"12th Joint Conference on Lexical and Computational Semantics","first-page":"128","article-title":"Generative data augmentation for aspect sentiment quad prediction","author":"Wang","year":"2023"},{"key":"10.1016\/j.datak.2026.102634_b7","series-title":"2023 International Joint Conference on Neural Networks","first-page":"1","article-title":"Boosting aspect sentiment quad prediction by data augmentation and self-training","author":"Yu","year":"2023"},{"key":"10.1016\/j.datak.2026.102634_b8","series-title":"2021 Conference on Empirical Methods in Natural Language Processing","first-page":"9209","article-title":"Aspect sentiment quad prediction as paraphrase generation","author":"Zhang","year":"2021"},{"issue":"5","key":"10.1016\/j.datak.2026.102634_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2023.103435","article-title":"Leveraging hierarchical language models for aspect-based sentiment analysis on financial data","volume":"60","author":"Lengkeek","year":"2023","journal-title":"Inf. Process. Manage."},{"key":"10.1016\/j.datak.2026.102634_b10","series-title":"FinBERT: Financial sentiment analysis with pre-trained language models","author":"Araci","year":"2019"},{"key":"10.1016\/j.datak.2026.102634_b11","series-title":"2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","first-page":"2324","article-title":"BERT post-training for review reading comprehension and aspect-based sentiment analysis","author":"Xu","year":"2019"},{"key":"10.1016\/j.datak.2026.102634_b12","series-title":"31st International Conference on Neural Information Processing Systems","first-page":"6000","article-title":"Attention is all uou need","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.datak.2026.102634_b13","series-title":"2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2019"},{"issue":"4","key":"10.1016\/j.datak.2026.102634_b14","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1002\/asi.23062","article-title":"Good debt or bad debt: Detecting semantic orientations in economic texts","volume":"65","author":"Malo","year":"2014","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"10.1016\/j.datak.2026.102634_b15","series-title":"7th International Conference on Language Resources and Evaluation","doi-asserted-by":"crossref","first-page":"2200","DOI":"10.63317\/2jktnrykui9s","article-title":"SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining","author":"Baccianella","year":"2010"},{"issue":"11","key":"10.1016\/j.datak.2026.102634_b16","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1145\/219717.219748","article-title":"WordNet: A lexical database for English","volume":"38","author":"Miller","year":"1995","journal-title":"Commun. ACM"},{"key":"10.1016\/j.datak.2026.102634_b17","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"10.1016\/j.datak.2026.102634_b18","series-title":"Classification and Regression Trees","author":"Breiman","year":"1984"},{"key":"10.1016\/j.datak.2026.102634_b19","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"issue":"4","key":"10.1016\/j.datak.2026.102634_b20","doi-asserted-by":"crossref","first-page":"65:1","DOI":"10.1145\/3503044","article-title":"A survey on aspect-based sentiment classification","volume":"55","author":"Brauwers","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.datak.2026.102634_b21","series-title":"RoBERTa: A robustly optimized BERT pretraining approach","author":"Liu","year":"2019"},{"key":"10.1016\/j.datak.2026.102634_b22","series-title":"7th International Conference on Learning Representations","article-title":"GLUE: A multi-task benchmark and analysis platform for natural language understanding","author":"Wang","year":"2019"},{"key":"10.1016\/j.datak.2026.102634_b23","series-title":"56th Annual Meeting of the Association for Computational Linguistics","first-page":"784","article-title":"Know what you don\u2019t know: Unanswerable questions for SQuAD","author":"Rajpurkar","year":"2018"},{"key":"10.1016\/j.datak.2026.102634_b24","series-title":"The Web Conference 2018","first-page":"1941","article-title":"WWW\u201918 open challenge: Financial opinion mining and question answering","author":"Maia","year":"2018"},{"key":"10.1016\/j.datak.2026.102634_b25","series-title":"Reuters corpora (RCV1, RCV2, TRC2)","author":"National Institute of Standards and Technology","year":"2025"},{"key":"10.1016\/j.datak.2026.102634_b26","series-title":"56th Annual Meeting of the Association for Computational Linguistics","first-page":"328","article-title":"Universal language model fine-tuning for text classification","author":"Howard","year":"2018"},{"key":"10.1016\/j.datak.2026.102634_b27","series-title":"LoRA: Low-rank adaptation of large language models","author":"Hu","year":"2021"},{"key":"10.1016\/j.datak.2026.102634_b28","series-title":"58th Annual Meeting of the Association for Computational Linguistics","first-page":"1106","article-title":"Hierarchy-aware global model for hierarchical text classification","author":"Zhou","year":"2020"},{"key":"10.1016\/j.datak.2026.102634_b29","series-title":"16th IEEE International Conference on Machine Learning and Applications","first-page":"364","article-title":"HDLTex: Hierarchical deep learning for text classification","author":"Kowsari","year":"2017"},{"key":"10.1016\/j.datak.2026.102634_b30","series-title":"33rd IEEE International Conference on Tools with Artificial Intelligence","first-page":"1138","article-title":"Hierarchical multilabel text classification via multitask learning","author":"Yu","year":"2021"},{"key":"10.1016\/j.datak.2026.102634_b31","article-title":"Hierarchical text classification: A review of current research","volume":"224","author":"Zangari","year":"2023","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"10.1016\/j.datak.2026.102634_b32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3343039","article-title":"Explaining financial uncertainty through specialized word embeddings","volume":"1","author":"Theil","year":"2020","journal-title":"ACM\/IMS Trans. Data Sci."},{"key":"10.1016\/j.datak.2026.102634_b33","doi-asserted-by":"crossref","DOI":"10.1093\/database\/bay066","article-title":"Improving the learning of chemical-protein interactions from literature using transfer learning and specialized word embeddings","volume":"2018","author":"Corbett","year":"2018","journal-title":"Database: J. Biol. Databases Curation"},{"issue":"11","key":"10.1016\/j.datak.2026.102634_b34","doi-asserted-by":"crossref","first-page":"2682","DOI":"10.4304\/jsw.8.11.2682-2687","article-title":"The opinion mining based on fuzzy domain sentiment ontology tree for product reviews","volume":"8","author":"Nie","year":"2013","journal-title":"J. Softw."},{"key":"10.1016\/j.datak.2026.102634_b35","series-title":"Exploring hierarchical linguistic structure for aspect-based sentiment analysis","first-page":"945","author":"Sanglerdsinlapachai","year":"2017"},{"issue":"1","key":"10.1016\/j.datak.2026.102634_b36","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1109\/JSYST.2018.2794462","article-title":"Forecasting stock market movement direction using sentiment analysis and support vector machine","volume":"13","author":"Ren","year":"2019","journal-title":"IEEE Syst. J."},{"key":"10.1016\/j.datak.2026.102634_b37","series-title":"2014 International Conference on Computer, Communications, and Control Technology","first-page":"333","article-title":"Boosting aspect sentiment quad prediction by data augmentation and self-training","author":"Zainuddin","year":"2014"},{"key":"10.1016\/j.datak.2026.102634_b38","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1016\/j.procs.2019.11.181","article-title":"Sentiment analysis of social media Twitter with case of anti-lgbt campaign in Indonesia using na\u00efve bayes, decision tree, and random forest algorithm","volume":"161","author":"Fitri","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.datak.2026.102634_b39","series-title":"8th International Workshop on Semantic Evaluation","first-page":"319","article-title":"IITP: Supervised machine learning for aspect based sentiment analysis","author":"Gupta","year":"2014"},{"key":"10.1016\/j.datak.2026.102634_b40","series-title":"25th International Conference on Computational Linguistics","first-page":"69","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"dos Santos","year":"2014"},{"key":"10.1016\/j.datak.2026.102634_b41","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.future.2020.08.005","article-title":"ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis","volume":"115","author":"Basiri","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.datak.2026.102634_b42","series-title":"2016 IEEE 6th International Conference on Advanced Computing","first-page":"3","article-title":"A feature based approach for sentiment analysis by using support vector machine","author":"Devi","year":"2016"},{"key":"10.1016\/j.datak.2026.102634_b43","doi-asserted-by":"crossref","first-page":"3221","DOI":"10.1007\/s00521-019-04105-z","article-title":"Aspect-based sentiment analysis using deep networks and stochastic optimization","volume":"32","author":"Kumar","year":"2020","journal-title":"Neural Comput. Appl."},{"issue":"10","key":"10.1016\/j.datak.2026.102634_b44","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"10.1016\/j.datak.2026.102634_b45","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1007\/s10462-022-10183-8","article-title":"State of the art: A review of sentiment analysis based on sequential transfer learning","volume":"56","author":"Chan","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.datak.2026.102634_b46","doi-asserted-by":"crossref","first-page":"85401","DOI":"10.1109\/ACCESS.2019.2925059","article-title":"A survey of sentiment analysis based on transfer learning","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.datak.2026.102634_b47","series-title":"2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","first-page":"2227","article-title":"Deep contextualized word representations","author":"Peters","year":"2018"},{"key":"10.1016\/j.datak.2026.102634_b48","article-title":"Improving language understanding by generative pre-training","author":"Radford","year":"2018","journal-title":"OpenAI"},{"key":"10.1016\/j.datak.2026.102634_b49","article-title":"Language models are unsupervised multitask learners","author":"Radford","year":"2019","journal-title":"OpenAI"},{"key":"10.1016\/j.datak.2026.102634_b50","series-title":"34th Annual Conference on Advances in Neural Information Processing Systems 2020","first-page":"1877","article-title":"Language models are few-shot learners","author":"Brown","year":"2020"},{"key":"10.1016\/j.datak.2026.102634_b51","series-title":"54th Annual Meeting of the Association for Computational Linguistics","first-page":"1715","article-title":"Neural machine translation of rare words with subword units","author":"Sennrich","year":"2016"},{"key":"10.1016\/j.datak.2026.102634_b52","series-title":"1st International Conference on Learning Representations","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov","year":"2013"},{"key":"10.1016\/j.datak.2026.102634_b53","series-title":"26th International Conference on Neural Information Processing Systems","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","author":"Mikolov","year":"2013"},{"key":"10.1016\/j.datak.2026.102634_b54","series-title":"2014 Conference on Empirical Methods in Natural Language Processing","first-page":"1532","article-title":"GloVe: Global vectors for word representation","author":"Pennington","year":"2014"},{"key":"10.1016\/j.datak.2026.102634_b55","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1162\/tacl_a_00051","article-title":"Enriching word vectors with subword information","volume":"5","author":"Bojanowski","year":"2017","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"10.1016\/j.datak.2026.102634_b56","series-title":"31st International Conference on Neural Information Processing Systems","first-page":"6297","article-title":"Learned in translation: Contextualized word vectors","author":"McCann","year":"2017"},{"key":"10.1016\/j.datak.2026.102634_b57","series-title":"Google\u2019s neural machine translation system: Bridging the gap between human and machine translation","author":"Wu","year":"2016"},{"key":"10.1016\/j.datak.2026.102634_b58","series-title":"International Conference on Advanced Multimedia and Ubiquitous Engineering\/Future Information Technology","first-page":"347","article-title":"Bayesian multinomial na\u00efve Bayes classifier to text classification","volume":"Vol. 448","author":"Xu","year":"2017"},{"key":"10.1016\/j.datak.2026.102634_b59","series-title":"5th International Conference on Learning Representations","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"key":"10.1016\/j.datak.2026.102634_b60","first-page":"361","article-title":"RCV1: A new benchmark collection for text categorization research","volume":"5","author":"Lewis","year":"2004","journal-title":"J. Mach. Learn. Res."},{"issue":"5","key":"10.1016\/j.datak.2026.102634_b61","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1007\/s10462-022-10252-y","article-title":"Survey on aspect detection for aspect-based sentiment analysis","volume":"56","author":"Trusca","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.datak.2026.102634_b62","series-title":"19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"257","article-title":"Recursive regularization for large-scale classification with hierarchical and graphical dependencies","author":"Gopal","year":"2013"},{"key":"10.1016\/j.datak.2026.102634_b63","series-title":"Entity embeddings of categorical variables","author":"Guo","year":"2016"},{"key":"10.1016\/j.datak.2026.102634_b64","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117871","article-title":"Modeling second language acquisition with pre-trained neural language models","volume":"207","author":"Palenzuela","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.datak.2026.102634_b65","series-title":"25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"2623","article-title":"Optuna: A next-generation hyperparameter optimization framework","author":"Akiba","year":"2019"},{"key":"10.1016\/j.datak.2026.102634_b66","series-title":"30th International Conference on Machine Learning","first-page":"115","article-title":"Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures","volume":"28","author":"Bergstra","year":"2013"},{"key":"10.1016\/j.datak.2026.102634_b67","series-title":"15th Conference of the European Chapter of the Association for Computational Linguistics","first-page":"157","article-title":"Using the output embedding to improve language models","author":"Press","year":"2017"},{"key":"10.1016\/j.datak.2026.102634_b68","series-title":"Financial aspect-based sentiment analysis using deep representations","author":"Yang","year":"2018"},{"key":"10.1016\/j.datak.2026.102634_b69","series-title":"The Web Conference 2018","first-page":"1967","article-title":"INF-UFG at FiQA 2018 task 1: Predicting sentiments and aspects on financial tweets and news headlines","author":"de Fran\u00e7a Costa","year":"2018"},{"key":"10.1016\/j.datak.2026.102634_b70","series-title":"The Web Conference 2018","first-page":"1973","article-title":"Financial aspect and sentiment predictions with deep neural networks: An ensemble approach","author":"Piao","year":"2018"},{"key":"10.1016\/j.datak.2026.102634_b71","series-title":"The Web Conference 2018","first-page":"1961","article-title":"Aspect-based financial sentiment analysis using deep learning","author":"Jangid","year":"2018"},{"key":"10.1016\/j.datak.2026.102634_b72","series-title":"8th International Conference on Learning Representations","article-title":"ALBERT: a lite BERT for self-supervised learning of language representations","author":"Lan","year":"2020"},{"key":"10.1016\/j.datak.2026.102634_b73","series-title":"33rd Annual Conference on Advances in Neural Information Processing Systems 2019","first-page":"5754","article-title":"XLNet: Generalized autoregressive pretraining for language understanding","author":"Yang","year":"2019"},{"key":"10.1016\/j.datak.2026.102634_b74","series-title":"11th International Conference on Learning Representations","article-title":"DeBERTaV3: Improving DeBERTa using ELECTRA-style pre-training with gradient-disentangled embedding sharing","author":"He","year":"2023"},{"key":"10.1016\/j.datak.2026.102634_b75","series-title":"2018 Conference on Empirical Methods in Natural Language Processing","first-page":"1109","article-title":"Joint aspect and polarity classification for aspect-based sentiment analysis with end-to-end neural networks","author":"Schmitt","year":"2018"},{"key":"10.1016\/j.datak.2026.102634_b76","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.neucom.2021.03.100","article-title":"End-to-end aspect-based sentiment analysis with hierarchical multi-task learning","volume":"455","author":"Wang","year":"2021","journal-title":"Neurocomputing"}],"container-title":["Data &amp; Knowledge Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169023X26000819?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169023X26000819?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:27:09Z","timestamp":1787099229000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169023X26000819"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":76,"alternative-id":["S0169023X26000819"],"URL":"https:\/\/doi.org\/10.1016\/j.datak.2026.102634","relation":{},"ISSN":["0169-023X"],"issn-type":[{"value":"0169-023X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Hierarchical aspect-based sentiment analysis Using FinRoBERTa","name":"articletitle","label":"Article Title"},{"value":"Data & Knowledge Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.datak.2026.102634","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"102634"}}