{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T02:33:05Z","timestamp":1762050785406,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T00:00:00Z","timestamp":1652400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Cardiff University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>(1) Background: Aspect-based sentiment analysis (SA) is a natural language processing task, the aim of which is to classify the sentiment associated with a specific aspect of a written text. The performance of SA methods applied to texts related to health and well-being lags behind that of other domains. (2) Methods: In this study, we present an approach to aspect-based SA of drug reviews. Specifically, we analysed signs and symptoms, which were extracted automatically using the Unified Medical Language System. This information was then passed onto the BERT language model, which was extended by two layers to fine-tune the model for aspect-based SA. The interpretability of the model was analysed using an axiomatic attribution method. We performed a correlation analysis between the attribution scores and syntactic dependencies. (3) Results: Our fine-tuned model achieved accuracy of approximately 95% on a well-balanced test set. It outperformed our previous approach, which used syntactic information to guide the operation of a neural network and achieved an accuracy of approximately 82%. (4) Conclusions: We demonstrated that a BERT-based model of SA overcomes the negative bias associated with health-related aspects and closes the performance gap against the state-of-the-art in other domains.<\/jats:p>","DOI":"10.3390\/make4020021","type":"journal-article","created":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T08:37:02Z","timestamp":1652431022000},"page":"474-487","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["The Case of Aspect in Sentiment Analysis: Seeking Attention or Co-Dependency?"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5222-1268","authenticated-orcid":false,"given":"Anastazia","family":"\u017duni\u0107","sequence":"first","affiliation":[{"name":"School of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9731-3385","authenticated-orcid":false,"given":"Padraig","family":"Corcoran","sequence":"additional","affiliation":[{"name":"School of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8132-3885","authenticated-orcid":false,"given":"Irena","family":"Spasi\u0107","sequence":"additional","affiliation":[{"name":"School of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, B. (2012). Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers.","DOI":"10.1007\/978-3-031-02145-9"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/MIS.2017.4531228","article-title":"Sentiment Analysis Is a Big Suitcase","volume":"32","author":"Cambria","year":"2017","journal-title":"IEEE Intell. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1007\/s00357-019-9307-0","article-title":"Comparing the utility of different classification schemes for emotive language analysis","volume":"36","author":"Williams","year":"2019","journal-title":"J. Classif."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1109\/TKDE.2015.2485209","article-title":"Survey on Aspect-Level Sentiment Analysis","volume":"28","author":"Schouten","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.jbi.2016.06.007","article-title":"Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts","volume":"62","author":"Korkontzelos","year":"2016","journal-title":"J. Biomed. Informat."},{"key":"ref_6","unstructured":"\u017duni\u0107, A., Corcoran, P., and Spasi\u0107, I. (2020, January 23\u201324). Improving the performance of sentiment analysis in health and wellbeing using domain knowledge. Proceedings of the HealTAC 2020: Healthcare Text Analytics Conference, London, UK."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e16023","DOI":"10.2196\/16023","article-title":"Sentiment analysis in health and well-being: Systematic review","volume":"8","author":"Corcoran","year":"2020","journal-title":"JMIR Med. Informat."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102138","DOI":"10.1016\/j.artmed.2021.102138","article-title":"Aspect-based sentiment analysis with graph convolution over syntactic dependencies","volume":"119","author":"Corcoran","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_9","first-page":"5998","article-title":"Attention is all you need","volume":"Volume 30","author":"Vaswani","year":"2017","journal-title":"Proceedings of the Advances in Neural Information Processing Systems (NIPS)"},{"key":"ref_10","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019, January 2\u20137). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Minneapolis, MN, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ruder, S., Ghaffari, P., and Breslin, J.G. (2016, January 1\u20135). A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), Austin, TX, USA.","DOI":"10.18653\/v1\/D16-1103"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Y., Huang, M., Zhu, X., and Zhao, L. (2016, January 1\u20135). Attention-based LSTM for Aspect-level Sentiment Classification. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), Austin, TX, USA.","DOI":"10.18653\/v1\/D16-1058"},{"key":"ref_13","unstructured":"Bao, L., Lambert, P., and Badia, T. (August, January 28). Attention and lexicon regularized LSTM for aspect-based sentiment analysis. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL): Student Research Workshop, Florence, Italy."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"21314","DOI":"10.1109\/ACCESS.2020.2969473","article-title":"Aspect-level drug reviews sentiment analysis based on double BiGRU and knowledge transfer","volume":"8","author":"Han","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","unstructured":"Yin, W., Kann, K., Yu, M., and Sch\u00fctze, H. (2022, March 14). Comparative Study of CNN and RNN for Natural Language Processing, Available online: http:\/\/xxx.lanl.gov\/abs\/1702.01923."},{"key":"ref_16","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2022, March 14). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling, Available online: http:\/\/xxx.lanl.gov\/abs\/1803.01271."},{"key":"ref_17","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":"ref_18","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.ins.2022.03.082","article-title":"Aggregated graph convolutional networks for aspect-based sentiment classification","volume":"600","author":"Zhao","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107736","DOI":"10.1016\/j.knosys.2021.107736","article-title":"Phrase dependency relational graph attention network for Aspect-based Sentiment Analysis","volume":"236","author":"Wu","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107643","DOI":"10.1016\/j.knosys.2021.107643","article-title":"Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks","volume":"235","author":"Liang","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2021.10.091","article-title":"Exploring fine-grained syntactic information for aspect-based sentiment classification with dual graph neural networks","volume":"471","author":"Xiao","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.neucom.2021.09.057","article-title":"BiERU: Bidirectional emotional recurrent unit for conversational sentiment analysis","volume":"467","author":"Li","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"\u00d6zbey, C., Dileko\u011flu, B., and A\u00e7iks\u00f6z, S. (2021, January 6\u20138). The Impact of Ensemble Learning in Sentiment Analysis under Domain Shift. Proceedings of the 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), Elazig, Turkey.","DOI":"10.1109\/ASYU52992.2021.9599078"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"115819","DOI":"10.1016\/j.eswa.2021.115819","article-title":"The power of ensemble learning in sentiment analysis","volume":"187","author":"Kazmaier","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_25","first-page":"63","article-title":"Research on Text Sentiment Analysis Based on Neural Network and Ensemble Learning","volume":"35","author":"Luo","year":"2021","journal-title":"Rev. D\u2019Intelligence Artif."},{"key":"ref_26","unstructured":"Sun, C., Huang, L., and Qiu, X. (2019, January 2\u20137). Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Minneapolis, MN, USA."},{"key":"ref_27","unstructured":"Xu, H., Liu, B., Shu, L., and Yu, P. (2019, January 2\u20137). BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), Volume 1 (Long and Short Papers), Minneapolis, MN, USA."},{"key":"ref_28","unstructured":"Hoang, M., Bihorac, O.A., and Rouces, J. (October, January 30). Aspect-based sentiment analysis using bert. Proceedings of the 22nd Nordic Conference on Computational Linguistics, Turku, Finland."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"46868","DOI":"10.1109\/ACCESS.2020.2978511","article-title":"Enhancing BERT Representation With Context-Aware Embedding for Aspect-Based Sentiment Analysis","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: A pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Alsentzer, E., Murphy, J., Boag, W., Weng, W.H., Jindi, D., Naumann, T., and McDermott, M. (2019, January 2\u20137). Publicly Available Clinical BERT Embeddings. Proceedings of the 2nd Clinical Natural Language Processing Workshop, Minneapolis, MN, USA.","DOI":"10.18653\/v1\/W19-1909"},{"key":"ref_32","first-page":"2360","article-title":"Aspect Based Twitter Sentiment Analysis on Vaccination and Vaccine Types in COVID-19 Pandemic with Deep Learning","volume":"26","author":"Kaya","year":"2021","journal-title":"IEEE J. Biomed. Health Informat."},{"key":"ref_33","unstructured":"Punith, N., and Raketla, K. (2021, January 2\u20134). Sentiment Analysis of Drug Reviews using Transfer Learning. Proceedings of the 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1111\/j.1467-8640.2006.00276.x","article-title":"The importance of neutral examples for learning sentiment","volume":"22","author":"Koppel","year":"2006","journal-title":"Comput. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.inffus.2018.03.007","article-title":"Consensus vote models for detecting and filtering neutrality in sentiment analysis","volume":"44","author":"Valdivia","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"D267","DOI":"10.1093\/nar\/gkh061","article-title":"The Unified Medical Language System (UMLS): Integrating biomedical terminology","volume":"32","author":"Bodenreider","year":"2004","journal-title":"Nucleic Acids Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., and Funtowicz, M. (2020, January 16\u201320). Transformers: State-of-the-Art Natural Language Processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP), Online.","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"ref_38","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR), Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_39","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201314). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Vancouver, BC, Canada."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gr\u00e4\u00dfer, F., Kallumadi, S., Malberg, H., and Zaunseder, S. (2018, January 23\u201326). Aspect-based sentiment analysis of drug reviews applying cross-domain and cross-data learning. Proceedings of the 2018 International Conference on Digital Health, Lyon, France.","DOI":"10.1145\/3194658.3194677"},{"key":"ref_41","unstructured":"(2022, March 02). Drugs.com. Available online: https:\/\/www.drugs.com\/."},{"key":"ref_42","unstructured":"Sanh, V., Debut, L., Chaumond, J., and Wolf, T. (2019). DistilBERT, a Distilled Version of BERT: Smaller, Faster, Cheaper and Lighter. arXiv, Available online: http:\/\/xxx.lanl.gov\/abs\/1910.01108."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ofek, N., Rokach, L., Caragea, C., and Yen, J. (2015, January 18\u201322). The Importance of Pronouns to Sentiment Analysis: Online Cancer Survivor Network Case Study. Proceedings of the 24th International Conference on World Wide Web (WWW), Florence, Italy.","DOI":"10.1145\/2740908.2742781"},{"key":"ref_44","unstructured":"Kokhlikyan, N., Miglani, V., Martin, M., Wang, E., Alsallakh, B., Reynolds, J., Melnikov, A., Kliushkina, N., Araya, C., and Yan, S. (2022, May 11). Captum: A Unified and Generic Model Interpretability Library for PyTorch, Available online: http:\/\/xxx.lanl.gov\/abs\/2009.07896."},{"key":"ref_45","unstructured":"Sundararajan, M., Taly, A., and Yan, Q. (2017, January 6\u201311). Axiomatic Attribution for Deep Networks. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_46","unstructured":"Htut, P.M., Phang, J., Bordia, S., and Bowman, S.R. (2019). Do Attention Heads in BERT Track Syntactic Dependencies?. arXiv, Available online: http:\/\/xxx.lanl.gov\/abs\/1911.12246."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ravishankar, V., Kulmizev, A., Abdou, M., S\u00f8gaard, A., and Nivre, J. (2021, January 19\u201323). Attention Can Reflect Syntactic Structure (If You Let It). Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (EACL), Online.","DOI":"10.18653\/v1\/2021.eacl-main.264"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Clark, K., Khandelwal, U., Levy, O., and Manning, C.D. (2019, January 1). What Does BERT Look at? An Analysis of BERT\u2019s Attention. Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, Florence, Italy.","DOI":"10.18653\/v1\/W19-4828"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"103974","DOI":"10.1016\/j.ijmedinf.2019.103974","article-title":"Emerging clinical applications of text analytics","volume":"134","author":"Uzuner","year":"2020","journal-title":"Int. J. Med Informat."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/000169937501800102","article-title":"Health and quality of life","volume":"18","author":"Berg","year":"1975","journal-title":"Acta Sociol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s13326-019-0215-3","article-title":"KLOSURE: Closing in on open\u2013ended patient questionnaires with text mining","volume":"10","author":"Owen","year":"2019","journal-title":"J. Biomed. Semant."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/4\/2\/21\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:10:18Z","timestamp":1760137818000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/4\/2\/21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,13]]},"references-count":51,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["make4020021"],"URL":"https:\/\/doi.org\/10.3390\/make4020021","relation":{},"ISSN":["2504-4990"],"issn-type":[{"type":"electronic","value":"2504-4990"}],"subject":[],"published":{"date-parts":[[2022,5,13]]}}}