{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T10:06:53Z","timestamp":1762337213617,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032104854"},{"type":"electronic","value":"9783032104861"}],"license":[{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-10486-1_12","type":"book-chapter","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T09:36:46Z","timestamp":1762335406000},"page":"120-130","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging Sentiment Analysis for Improved Financial Distress Forecasting in Vietnamese-Listed Firms"],"prefix":"10.1007","author":[{"given":"Pham Van","family":"Thanh","sequence":"first","affiliation":[]},{"given":"Phan Duy","family":"Hung","sequence":"additional","affiliation":[]},{"given":"Truong Cong","family":"Doan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,6]]},"reference":[{"issue":"4","key":"12_CR1","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1111\/j.1540-6261.1968.tb00843.x","volume":"23","author":"EI Altman","year":"1968","unstructured":"Altman, E.I.: Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J. Finance 23(4), 589\u2013609 (1968). https:\/\/doi.org\/10.1111\/j.1540-6261.1968.tb00843.x","journal-title":"J. Finance"},{"key":"12_CR2","doi-asserted-by":"publisher","unstructured":"C\u0131nd\u0131k, Z., Armutlulu, I.H., C\u0131nd\u0131k, Z., Armutlulu, I.H.: A revision of Altman Z-score model and a comparative analysis of Turkish companies\u2019 financial distress prediction. NAR 3(2), Article no. NAR-03-02-012 (2021). https:\/\/doi.org\/10.3934\/NAR.2021012","DOI":"10.3934\/NAR.2021012"},{"key":"12_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2022.113814","volume":"159","author":"D Wu","year":"2022","unstructured":"Wu, D., Ma, X., Olson, D.L.: Financial distress prediction using integrated Z-score and multilayer perceptron neural networks. Decis. Support. Syst. 159, 113814 (2022). https:\/\/doi.org\/10.1016\/j.dss.2022.113814","journal-title":"Decis. Support. Syst."},{"key":"12_CR4","doi-asserted-by":"publisher","unstructured":"Tran, T., Nguyen, N.H., Le, B.T., Vu, N.T., Vo, D.H.: Examining financial distress of the Vietnamese listed firms using accounting-based models. PLoS ONE 18(5), e0284451 (2023). thg 5. https:\/\/doi.org\/10.1371\/journal.pone.0284451","DOI":"10.1371\/journal.pone.0284451"},{"key":"12_CR5","unstructured":"Altman, E.: Predicting financial distress of companies: revisiting the Z-score and zeta. In: Handbook of Research Methods and Applications in Empirical Finance (2000). https:\/\/www.researchgate.net\/publication\/2413921_Predicting_Financial_Distress_Of_Companies_Revisiting_The_Z-Score_And_Zeta. Accessed 13 Mar 2025"},{"issue":"1","key":"12_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcae.2024.100403","volume":"20","author":"MJ Rahman","year":"2024","unstructured":"Rahman, M.J., Zhu, H.: Predicting financial distress using machine learning approaches: evidence China. J. Contemp. Account. Econ. 20(1), 100403 (2024). https:\/\/doi.org\/10.1016\/j.jcae.2024.100403","journal-title":"J. Contemp. Account. Econ."},{"issue":"4","key":"12_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2024.103703","volume":"61","author":"W Che","year":"2024","unstructured":"Che, W., Wang, Z., Jiang, C., Abedin, M.Z.: Predicting financial distress using multimodal data: an attentive and regularized deep learning method. Inf. Process. Manag. 61(4), 103703 (2024). https:\/\/doi.org\/10.1016\/j.ipm.2024.103703","journal-title":"Inf. Process. Manag."},{"key":"12_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106152","volume":"90","author":"G Wang","year":"2020","unstructured":"Wang, G., Ma, J., Chen, G., Yang, Y.: Financial distress prediction: regularized sparse-based random subspace with ER aggregation rule incorporating textual disclosures. Appl. Soft Comput. 90, 106152 (2020). https:\/\/doi.org\/10.1016\/j.asoc.2020.106152","journal-title":"Appl. Soft Comput."},{"key":"12_CR9","doi-asserted-by":"publisher","unstructured":"Zhang, M., Chen, J., Palade, V.: Fine-grained sentiment analysis for enhanced financial distress prediction. In: 2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), pp. 170\u2013180, February 2024. https:\/\/doi.org\/10.1109\/EEBDA60612.2024.10485942","DOI":"10.1109\/EEBDA60612.2024.10485942"},{"key":"12_CR10","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.eswa.2019.04.071","volume":"132","author":"R Matin","year":"2019","unstructured":"Matin, R., Hansen, C., Hansen, C., M\u00f8lgaard, P.: Predicting distresses using deep learning of text segments in annual reports. Expert Syst. Appl. 132, 199\u2013208 (2019). https:\/\/doi.org\/10.1016\/j.eswa.2019.04.071","journal-title":"Expert Syst. Appl."},{"key":"12_CR11","doi-asserted-by":"publisher","unstructured":"Tran, K.L., Le, H.A., Nguyen, T.H., Nguyen, D.T.: Explainable machine learning for financial distress prediction: evidence from Vietnam. Data 7(11), Article no. 11 (2022). https:\/\/doi.org\/10.3390\/data7110160","DOI":"10.3390\/data7110160"},{"key":"12_CR12","unstructured":"VietstockFinance: \u201cBusiness AZ,\u201d VietstockFinance. https:\/\/finance.vietstock.vn\/doanh-nghiep-a-z?languageid=2. Accessed 14 Mar 2025"},{"key":"12_CR13","doi-asserted-by":"publisher","unstructured":"Nguyen, D.Q., Nguyen, A.T.: PhoBERT: pre-trained language models for Vietnamese. In: Findings of the Association for Computational Linguistics: EMNLP 2020, January 2020. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.92","DOI":"10.18653\/v1\/2020.findings-emnlp.92"},{"key":"12_CR14","unstructured":"thuvienphapluat.vn: Decision 17\/QD-HDTV 2022 Regulation for Listing Securities and Trading of Listed Securities. TH\u01af VI\u1ec6N PH\u00c1P LU\u1eacT. https:\/\/thuvienphapluat.vn\/van-ban\/EN\/Chung-khoan\/Decision-17-QD-HDTV-2022-Regulation-for-Listing-Securities-and-Trading-of-Listed-Securities\/583887\/tieng-anh.aspx. Accessed 14 Mar 2025"},{"issue":"4","key":"12_CR15","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1111\/acfi.12432","volume":"58","author":"Y Jiang","year":"2018","unstructured":"Jiang, Y., Jones, S.: Corporate distress prediction in China: a machine learning approach. Account. Finance 58(4), 1063\u20131109 (2018). https:\/\/doi.org\/10.1111\/acfi.12432","journal-title":"Account. Finance"},{"issue":"3","key":"12_CR16","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995). https:\/\/doi.org\/10.1007\/BF00994018","journal-title":"Mach. Learn."},{"issue":"1","key":"12_CR17","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random Forests. Mach. Learn. 45(1), 5\u201332 (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"issue":"5","key":"12_CR18","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Statist. 29(5), 1189\u20131232 (2001). https:\/\/doi.org\/10.1214\/aos\/1013203451","journal-title":"Ann. Statist."},{"key":"12_CR19","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002). https:\/\/doi.org\/10.1613\/jair.953","journal-title":"J. Artif. Intell. Res."},{"issue":"2","key":"12_CR20","doi-asserted-by":"publisher","first-page":"2784","DOI":"10.1109\/TCSS.2023.3276059","volume":"11","author":"Y Chen","year":"2024","unstructured":"Chen, Y., Kuang, X., Guo, J.: LiFoL: an efficient framework for financial distress prediction in high-dimensional unbalanced scenario. IEEE Trans. Comput. Soc. Syst. 11(2), 2784\u20132795 (2024). https:\/\/doi.org\/10.1109\/TCSS.2023.3276059","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"12_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2018.05.026","volume":"110","author":"H Choi","year":"2018","unstructured":"Choi, H., Son, H., Kim, C.: Predicting financial distress of contractors in the construction industry using ensemble learning. Expert Syst. Appl. 110, 1 (2018). https:\/\/doi.org\/10.1016\/j.eswa.2018.05.026","journal-title":"Expert Syst. Appl."},{"issue":"85","key":"12_CR22","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(85), 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-10486-1_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T10:03:05Z","timestamp":1762336985000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-10486-1_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,6]]},"ISBN":["9783032104854","9783032104861"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-10486-1_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,11,6]]},"assertion":[{"value":"6 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDEAL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Data Engineering and Automated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ja\u00e9n","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ideal2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ideal2025.ujaen.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}