{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T19:06:51Z","timestamp":1789067211438,"version":"build-2803163510"},"reference-count":72,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"vor","delay-in-days":16,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["iaap-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Applied Psych Health &amp;amp; Well"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>For effective management of extreme rainstorm disasters, a timely understanding of the public emotional perception of risk is crucial to enhancing governmental risk communication and emergency response strategies. Therefore, this study develops a sentiment analysis framework that combines a rainstorm\u2010specific sentiment lexicon with a deep learning model. By utilizing large\u2010scale social media data, the framework further achieves dynamic monitoring and early warning of the public emotional perception of risk during extreme rainstorm events. Specifically, this paper employed text mining techniques to analyze the emotional features of 51,222 Weibo posts related to rainstorm disasters, thereby constructing a specialized sentiment lexicon. The lexicon was then integrated into a TextCNN model to create a knowledge\u2010enhanced hybrid sentiment analysis model. Compared with baseline models including GPT\u20104o, LLaMA\u20103, and RoBERTa, this hybrid method achieved optimal performance across all evaluation metrics, with accuracy and F1\u2010score improvements of 10.9% and 9.9%, respectively. Moreover, an empirical analysis of the 2023 Zhuozhou extreme rainstorm disaster validated the framework's efficacy. Findings reveal that our method can effectively monitor the public emotional perception of risk and provide early warning of risk anomalies during severe rainstorms by using the emotion index, which yields valuable insights for governmental bodies to accurately understand public risk perception and the dynamic evolution of disaster scenarios.<\/jats:p>","DOI":"10.1111\/aphw.70101","type":"journal-article","created":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T12:39:38Z","timestamp":1765975178000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dynamic monitoring and early warning of public emotional perception of risk during extreme rainstorm disasters: A study based on social media"],"prefix":"10.1111","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4245-724X","authenticated-orcid":false,"given":"Zunxiang","family":"Qiu","sequence":"first","affiliation":[{"name":"School of Economics and Management China University of Mining and Technology  Xuzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4948-1786","authenticated-orcid":false,"given":"Xinchun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Economics and Management China University of Mining and Technology  Xuzhou China"},{"name":"Key Construction Bases of Philosophy and Social Sciences in Jiangsu Universities (Safety Management Research Center), China University of Mining and Technology  Xuzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8789-7660","authenticated-orcid":false,"given":"Quanlong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Economics and Management China University of Mining and Technology  Xuzhou China"},{"name":"Key Construction Bases of Philosophy and Social Sciences in Jiangsu Universities (Safety Management Research Center), China University of Mining and Technology  Xuzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.iswa.2022.200090"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/risa.13639"},{"issue":"2","key":"e_1_2_11_4_1","first-page":"97","article-title":"Social media integration of flood data: A vine copula\u2010based approach","volume":"39","author":"Ansell L.","year":"2022","journal-title":"Journal of Environmental Informatics"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2021.126506"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118710"},{"key":"e_1_2_11_7_1","unstructured":"Arnold M. B.(1960).Emotion and personality"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-021-09833-w"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2023.105405"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106876"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2022.104663"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108781"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/risa.13206"},{"key":"e_1_2_11_14_1","doi-asserted-by":"crossref","unstructured":"Devlin J. Chang M. W. Lee K. &Toutanova K.(2019).Bert: Pre\u2010training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies volume 1 (long and short papers) (pp. 4171\u20104186)","DOI":"10.18653\/v1\/N19-1423"},{"key":"e_1_2_11_15_1","first-page":"162","volume-title":"COVID\u201019","author":"Dryhurst S.","year":"2022"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1099-0771(200001\/03)13:1<1::AID-BDM333>3.0.CO;2-S"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2021.4156"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2923637"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.2196\/51069"},{"issue":"3","key":"e_1_2_11_20_1","first-page":"249","article-title":"An analysis of statistical techniques applied to lexical differences between corpora","volume":"33","author":"Ge S.","year":"2010","journal-title":"Modern Foreign Languages"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122454"},{"key":"e_1_2_11_22_1","unstructured":"Grattafiori A. Dubey A. Jauhri A. Pandey A. Kadian A. Al\u2010Dahle A. Letman A. Mathur A. Schelten A. Vaughan A. Yang A. Fan A. Goyal A. Hartshorn A. Yang A. Mitra A. Sravankumar A. Korenev A. Hinsvark A \u2026Ma Z. (2024).The Llama 3 herd of models. arXiv Preprint arXiv:2407.21783."},{"key":"e_1_2_11_23_1","unstructured":"He P. Gao J. &Chen W.(2021).Debertav3: Improving deberta using electra\u2010style pre\u2010training with gradient\u2010disentangled embedding sharing. arXiv preprint arXiv:2111.09543"},{"issue":"2","key":"e_1_2_11_24_1","article-title":"Lora: Low\u2010rank adaptation of large language models","volume":"1","author":"Hu E. J.","year":"2022","journal-title":"ICLR"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.02.026"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2020.2997769"},{"key":"e_1_2_11_27_1","unstructured":"Hurst A Lerer A Goucher A. P Perelman A Ramesh A Clark A Ostrow A. J Welihinda A Hayes A Radford A. &Mdry A.(2024).Gpt\u20104o system card. arXiv preprint arXiv:2410.21276"},{"key":"e_1_2_11_28_1","doi-asserted-by":"publisher","DOI":"10.1080\/13669877.2023.2208149"},{"key":"e_1_2_11_29_1","doi-asserted-by":"crossref","unstructured":"Kim Y.(2014).Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.","DOI":"10.3115\/v1\/D14-1181"},{"key":"e_1_2_11_30_1","doi-asserted-by":"publisher","DOI":"10.22190\/FUME230901038K"},{"key":"e_1_2_11_31_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1432-2277.2005.00183.x"},{"issue":"10","key":"e_1_2_11_32_1","first-page":"2634","article-title":"Macro\u2010structural emotion analysis of social risk events in Weibo","volume":"42","author":"Leng J.","year":"2022","journal-title":"Journal of Systems Science and Mathematical Sciences"},{"key":"e_1_2_11_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102673"},{"key":"e_1_2_11_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106435"},{"key":"e_1_2_11_35_1","unstructured":"Liu Y Ott M Goyal N Du J Joshi M Chen D Levy O Lewis M Zettlemoyer L. &Stoyanov V.(2019).Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692."},{"key":"e_1_2_11_36_1","doi-asserted-by":"publisher","DOI":"10.1037\/0033-2909.127.2.267"},{"key":"e_1_2_11_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolecon.2022.107502"},{"key":"e_1_2_11_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2023.106138"},{"key":"e_1_2_11_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11069-023-06033-7"},{"key":"e_1_2_11_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-016-1308-8"},{"key":"e_1_2_11_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-021-10107-x"},{"issue":"13","key":"e_1_2_11_42_1","first-page":"91","article-title":"Research on the impact of COVlD\u201019 science information dissemination on social media and the factors influencing the choice of crisis coping strategies: An empirical analysis based on popular micro\u2010blog texts of scientists' groups","volume":"66","author":"Meng S.","year":"2022","journal-title":"Library and Information Service"},{"key":"e_1_2_11_43_1","doi-asserted-by":"publisher","DOI":"10.1111\/risa.13245"},{"key":"e_1_2_11_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103011"},{"key":"e_1_2_11_45_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2018.3226"},{"key":"e_1_2_11_46_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0272-4332.2004.00531.x"},{"key":"e_1_2_11_47_1","doi-asserted-by":"publisher","DOI":"10.1111\/risa.13946"},{"key":"e_1_2_11_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3246162"},{"key":"e_1_2_11_49_1","unstructured":"Sanh V. Debut L. Chaumond J. &Wolf T.(2019).DistilBERT a distilled version of BERT: Smaller faster cheaper and lighter. arXiv preprint arXiv:1910.01108."},{"key":"e_1_2_11_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2019.02.029"},{"key":"e_1_2_11_51_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103187"},{"key":"e_1_2_11_52_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1539-6924.2008.01049.x"},{"key":"e_1_2_11_53_1","doi-asserted-by":"publisher","DOI":"10.4324\/9781849776677"},{"key":"e_1_2_11_54_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8721.2006.00461.x"},{"key":"e_1_2_11_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2023.104553"},{"key":"e_1_2_11_56_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2024.131129"},{"key":"e_1_2_11_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2023.101776"},{"key":"e_1_2_11_58_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1539-6924.2011.01616.x"},{"key":"e_1_2_11_59_1","doi-asserted-by":"publisher","DOI":"10.1111\/risa.12575"},{"key":"e_1_2_11_60_1","volume-title":"Exploratory data analysis","author":"Tukey J. W.","year":"1977"},{"key":"e_1_2_11_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2021.121070"},{"key":"e_1_2_11_62_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijdrr.2023.104175"},{"key":"e_1_2_11_63_1","doi-asserted-by":"publisher","DOI":"10.1111\/risa.13207"},{"key":"e_1_2_11_64_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11069-020-04106-5"},{"issue":"12","key":"e_1_2_11_65_1","first-page":"3381","article-title":"The construction of inflation public opinion dictionary and index based on trendand sentiment mapping","volume":"42","author":"Xu P.","year":"2022","journal-title":"Systems Engineering \u2010 Theory & Practice"},{"key":"e_1_2_11_66_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2023.105077"},{"key":"e_1_2_11_67_1","unstructured":"Yang Z. Dai Z. Yang Y. Carbonell J. Salakhutdinov R. R. &Le Q. V.(2019).Xlnet: Generalized autoregressive pretraining for language understanding. Advances in Neural Information Processing Systems 32"},{"issue":"9","key":"e_1_2_11_68_1","first-page":"11","article-title":"Emotional evolution of public opinion in public health emergencies based on use rattention\u2010taking the COVID\u201019 epidemic as an example","volume":"39","author":"Zeng Z.","year":"2021","journal-title":"Information Science"},{"key":"e_1_2_11_69_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103378"},{"key":"e_1_2_11_70_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2022.101819"},{"key":"e_1_2_11_71_1","doi-asserted-by":"crossref","unstructured":"Zhang W. Deng Y. Liu B. Pan S. J. &Bing L.(2023).Sentiment analysis in the era of large language models: A reality check. arXiv preprint arXiv:2305.15005.","DOI":"10.18653\/v1\/2024.findings-naacl.246"},{"key":"e_1_2_11_72_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106136"},{"issue":"10","key":"e_1_2_11_73_1","first-page":"340","article-title":"Research on early warning of typhoon disasters based on social media datacase study of typhoon lekima","volume":"33","author":"Zheng S.","year":"2021","journal-title":"Management Review"}],"container-title":["Applied Psychology: Health and Well-Being"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/iaap-journals.onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/aphw.70101","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T07:15:38Z","timestamp":1766474138000},"score":1,"resource":{"primary":{"URL":"https:\/\/iaap-journals.onlinelibrary.wiley.com\/doi\/10.1111\/aphw.70101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":72,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["10.1111\/aphw.70101"],"URL":"https:\/\/doi.org\/10.1111\/aphw.70101","archive":["Portico"],"relation":{},"ISSN":["1758-0846","1758-0854"],"issn-type":[{"value":"1758-0846","type":"print"},{"value":"1758-0854","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"2025-06-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-07","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70101"}}