{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T04:05:12Z","timestamp":1749960312755,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819682973","type":"print"},{"value":"9789819682980","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-8298-0_18","type":"book-chapter","created":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T18:21:55Z","timestamp":1749925315000},"page":"221-232","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Corporate Carbon Emission Prediction: Combining Structured and\u00a0Unstructured Data"],"prefix":"10.1007","author":[{"given":"Jiaguan","family":"Shen","sequence":"first","affiliation":[]},{"given":"Weiyu","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,15]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Ahmad, H.O., Umar, S.U.: Sentiment analysis of financial textual data using machine learning and deep learning models. Informatica 47(5) (2023)","DOI":"10.31449\/inf.v47i5.4673"},{"issue":"4","key":"18_CR2","doi-asserted-by":"publisher","first-page":"3391","DOI":"10.3390\/su15043391","volume":"15","author":"J Assael","year":"2023","unstructured":"Assael, J., Heurtebize, T., Carlier, L., Soup\u00e9, F.: Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning. Sustainability 15(4), 3391 (2023)","journal-title":"Sustainability"},{"issue":"2","key":"18_CR3","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltru\u0161aitis","year":"2018","unstructured":"Baltru\u0161aitis, T., Ahuja, C., Morency, L.P.: Multimodal machine learning: a survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 423\u2013443 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"18_CR4","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.ejor.2022.06.060","volume":"306","author":"P Borchert","year":"2023","unstructured":"Borchert, P., Coussement, K., De Caigny, A., De Weerdt, J.: Extending business failure prediction models with textual website content using deep learning. Eur. J. Oper. Res. 306(1), 348\u2013357 (2023)","journal-title":"Eur. J. Oper. Res."},{"issue":"5","key":"18_CR5","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1111\/jiec.12522","volume":"21","author":"B Goldhammer","year":"2017","unstructured":"Goldhammer, B., Busse, C., Busch, T.: Estimating corporate carbon footprints with externally available data. J. Ind. Ecol. 21(5), 1165\u20131179 (2017)","journal-title":"J. Ind. Ecol."},{"issue":"2","key":"18_CR6","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1111\/1911-3846.12298","volume":"34","author":"PA Griffin","year":"2017","unstructured":"Griffin, P.A., Lont, D.H., Sun, E.Y.: The relevance to investors of greenhouse gas emission disclosures. Contemp. Account. Res. 34(2), 1265\u20131297 (2017)","journal-title":"Contemp. Account. Res."},{"key":"18_CR7","unstructured":"Hadziosmanovic, M., Kheradmand, E., Benguettat, N., Matthews, H.D., Lloyd, S.M.: Estimating corporate scope 1 emissions using tree-based machine learning methods. In: NeurIPS 2022 Workshop (2022)"},{"key":"18_CR8","unstructured":"Han, Y., Gopal, A., Ouyang, L., Key, A.: Estimation of corporate greenhouse gas emissions via machine learning. arXiv preprint arXiv:2109.04318 (2021)"},{"key":"18_CR9","unstructured":"Jiang, C., Wang, Z., Liu, X., et\u00a0al.: Capturing heterogeneous interactions for financial risk prediction of SMEs. In: Proceedings of the Pacific Asia Conference on Information Systems (2022)"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"John, A., Latha, T.: Stock market prediction based on deep hybrid RNN model and sentiment analysis. Automatika: \u010dasopis za automatiku, mjerenje, elektroniku, ra\u010dunarstvo i komunikacije 64(4), 981\u2013995 (2023)","DOI":"10.1080\/00051144.2023.2217602"},{"key":"18_CR11","doi-asserted-by":"publisher","first-page":"139504","DOI":"10.1016\/j.jclepro.2023.139504","volume":"429","author":"H Le","year":"2023","unstructured":"Le, H., Azhgaliyeva, D.: Carbon pricing and firms\u2019 GHG emissions: firm-level empirical evidence from East Asia. J. Clean. Prod. 429, 139504 (2023)","journal-title":"J. Clean. Prod."},{"issue":"7553","key":"18_CR12","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"issue":"6","key":"18_CR13","doi-asserted-by":"publisher","first-page":"9381","DOI":"10.1007\/s11042-022-13743-w","volume":"82","author":"C Lu","year":"2023","unstructured":"Lu, C., Zhou, G., Li, M.: Research on information fusion method for heat model and weather model based on HOGA-SVM. Multimedia Tools Appl. 82(6), 9381\u20139398 (2023)","journal-title":"Multimedia Tools Appl."},{"issue":"2","key":"18_CR14","doi-asserted-by":"publisher","first-page":"101","DOI":"10.3905\/jfi.2021.1.121","volume":"31","author":"G Manzo","year":"2021","unstructured":"Manzo, G., Qiao, X.: Deep learning credit risk modeling. J. Fixed Income 31(2), 101\u2013127 (2021)","journal-title":"J. Fixed Income"},{"issue":"3","key":"18_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439726","volume":"54","author":"S Minaee","year":"2021","unstructured":"Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., Gao, J.: Deep learning-based text classification: a comprehensive review. ACM Comput. Surv. (CSUR) 54(3), 1\u201340 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"18_CR16","unstructured":"NASA: Climate change evidence. https:\/\/science.nasa.gov\/climate-change\/evidence\/. Accessed 18 Apr 2024"},{"key":"18_CR17","doi-asserted-by":"publisher","first-page":"105129","DOI":"10.1016\/j.eneco.2021.105129","volume":"95","author":"Q Nguyen","year":"2021","unstructured":"Nguyen, Q., Diaz-Rainey, I., Kuruppuarachchi, D.: Predicting corporate carbon footprints for climate finance risk analyses: a machine learning approach. Energy Econ. 95, 105129 (2021)","journal-title":"Energy Econ."},{"issue":"1","key":"18_CR18","doi-asserted-by":"publisher","first-page":"16724","DOI":"10.1038\/s41598-018-35068-1","volume":"8","author":"G Strona","year":"2018","unstructured":"Strona, G., Bradshaw, C.J.: Co-extinctions annihilate planetary life during extreme environmental change. Sci. Rep. 8(1), 16724 (2018)","journal-title":"Sci. Rep."},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Tang, J., et al.: CarbonNet: enterprise-level carbon emission prediction with large-scale datasets. In: International Conference on Intelligent Computing, pp. 411\u2013422. Springer (2024)","DOI":"10.1007\/978-981-97-5615-5_33"},{"issue":"3","key":"18_CR20","doi-asserted-by":"publisher","first-page":"e0282234","DOI":"10.1371\/journal.pone.0282234","volume":"18","author":"S Usmani","year":"2023","unstructured":"Usmani, S., Shamsi, J.A.: LSTM based stock prediction using weighted and categorized financial news. PLoS ONE 18(3), e0282234 (2023)","journal-title":"PLoS ONE"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"Yan, Y., Qin, Q.: Sentiment analysis of financial media commentary text based on finbert-bigru-CNN. In: Proceedings of the 2024 International Conference on Smart City and Information System, p.\u00a01 (2024)","DOI":"10.1145\/3685088.3685187"}],"container-title":["Lecture Notes in Computer Science","Data Science: Foundations and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-8298-0_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T18:21:59Z","timestamp":1749925319000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-8298-0_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819682973","9789819682980"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-8298-0_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"15 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","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":"10 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}