{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T08:29:48Z","timestamp":1742977788633,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031647475"},{"type":"electronic","value":"9783031647482"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-64748-2_6","type":"book-chapter","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T07:03:08Z","timestamp":1721890988000},"page":"119-143","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unifying Economic and\u00a0Language Models for\u00a0Enhanced Sentiment Analysis of\u00a0the\u00a0Oil Market"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1115-8669","authenticated-orcid":false,"given":"Himmet","family":"Kaplan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6248-714X","authenticated-orcid":false,"given":"Ralf-Peter","family":"Mundani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9141-0886","authenticated-orcid":false,"given":"Heiko","family":"R\u00f6lke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6399-045X","authenticated-orcid":false,"given":"Albert","family":"Weichselbraun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4093-387X","authenticated-orcid":false,"given":"Martin","family":"Tschudy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,26]]},"reference":[{"key":"6_CR1","unstructured":"Araci, D.: FinBERT: financial sentiment analysis with pre-trained language models. arXiv:1908.10063 [cs] (2019)"},{"key":"6_CR2","doi-asserted-by":"publisher","unstructured":"Baboshkin, P., Uandykova, M.: Multi-source model of heterogeneous data analysis for oil price forecasting. Int. J. Energy Econ. Policy 11(2), 384\u2013391 (2021). https:\/\/doi.org\/10.32479\/ijeep.10853. https:\/\/econjournals.com\/index.php\/ijeep\/article\/view\/10853","DOI":"10.32479\/ijeep.10853"},{"key":"6_CR3","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 [cs, stat] (2016)"},{"key":"6_CR4","doi-asserted-by":"publisher","unstructured":"Balaji, S.N., Paul, P.V., Saravanan, R.: Survey on sentiment analysis based stock prediction using big data analytics. In: 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), pp.\u00a01\u20135. IEEE (2017). https:\/\/doi.org\/10.1109\/IPACT.2017.8244943","DOI":"10.1109\/IPACT.2017.8244943"},{"key":"6_CR5","doi-asserted-by":"publisher","unstructured":"Brown, T.B., et al.: Language models are few-shot learners (2020). https:\/\/doi.org\/10.48550\/ARXIV.2005.14165, publisher: arXiv Version Number: 4","DOI":"10.48550\/ARXIV.2005.14165"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Buyuksahin, B., Harris, J.: Do speculators drive crude oil futures prices? Energy J. 32(2), 167\u2013202 (2011). https:\/\/EconPapers.repec.org\/RePEc:aen:journl:2011v32-02-a07","DOI":"10.5547\/ISSN0195-6574-EJ-Vol32-No2-7"},{"key":"6_CR7","unstructured":"Chollet, F.: Deep Learning with Python. Manning Publications Co., Shelter Island, New York (2018). oCLC: ocn982650571"},{"key":"6_CR8","doi-asserted-by":"publisher","unstructured":"Cui, J., Wang, Z., Ho, S.B., Cambria, E.: Survey on sentiment analysis: evolution of research methods and topics. Artif. Intell. Rev. 1\u201342 (2023). https:\/\/doi.org\/10.1007\/s10462-022-10386-z","DOI":"10.1007\/s10462-022-10386-z"},{"key":"6_CR9","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 [cs] (2018)"},{"key":"6_CR10","doi-asserted-by":"publisher","unstructured":"Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Financ. 25(2), 383 (1970). https:\/\/doi.org\/10.2307\/2325486. https:\/\/www.jstor.org\/stable\/2325486?origin=crossref","DOI":"10.2307\/2325486"},{"issue":"22","key":"6_CR11","doi-asserted-by":"publisher","first-page":"8142","DOI":"10.3390\/app10228142","volume":"10","author":"Y Gu","year":"2020","unstructured":"Gu, Y., Shibukawa, T., Kondo, Y., Nagao, S., Kamijo, S.: Prediction of stock performance using deep neural networks. Appl. Sci. 10(22), 8142 (2020). https:\/\/doi.org\/10.3390\/app10228142","journal-title":"Appl. Sci."},{"key":"6_CR12","unstructured":"Hafez, P., Matas, R., Grinis, I., Gomez, F., Kangrga, M., Liu, A.: Factor Investing With Sentiment: A Look at Asia-Pacific Markets. White Paper (2020). https:\/\/www.ravenpack.com\/research\/news-sentiment-factor-enhancing-quantitative-investment-strategies-asia-pacific-stocks"},{"key":"6_CR13","doi-asserted-by":"publisher","unstructured":"Hamilton, J.: Understanding crude oil prices. Technical report. w14492, National Bureau of Economic Research, Cambridge (2008). https:\/\/doi.org\/10.3386\/w14492. http:\/\/www.nber.org\/papers\/w14492.pdf","DOI":"10.3386\/w14492"},{"issue":"1","key":"6_CR14","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3390\/asi4010009","volume":"4","author":"Z Hu","year":"2021","unstructured":"Hu, Z., Zhao, Y., Khushi, M.: A survey of forex and stock price prediction using deep learning. Appl. Syst. Innov. 4(1), 9 (2021)","journal-title":"Appl. Syst. Innov."},{"key":"6_CR15","unstructured":"HuggingFace: Crudebert. https:\/\/huggingface.co\/Captain-1337\/CrudeBERT. Accessed 20 Sept 2023"},{"key":"6_CR16","doi-asserted-by":"publisher","unstructured":"Jiang, H., He, P., Chen, W., Liu, X., Gao, J., Zhao, T.: SMART: robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 2177\u20132190 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.197. http:\/\/arxiv.org\/abs\/1911.03437","DOI":"10.18653\/v1\/2020.acl-main.197"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Kaplan, H., Mundani, R.P., R\u00f6lke, H., Weichselbraun, A.: CrudeBERT: applying economic theory towards fine-tuning transformer-based sentiment analysis models to the crude oil market. In: 25th International Conference on Enterprise Information Systems, Prague, Czech Republic (2023)","DOI":"10.5220\/0011749600003467"},{"key":"6_CR18","doi-asserted-by":"publisher","unstructured":"Leippold, M.: Sentiment spin: attacking financial sentiment with GPT-3. SSRN Electron. J. (2023). https:\/\/doi.org\/10.2139\/ssrn.4337182. https:\/\/www.ssrn.com\/abstract=4337182","DOI":"10.2139\/ssrn.4337182"},{"key":"6_CR19","doi-asserted-by":"publisher","unstructured":"Li, X., Xie, H., Chen, L., Wang, J., Deng, X.: News impact on stock price return via sentiment analysis. Knowl.-Based Syst. 69, 14\u201323 (2014). https:\/\/doi.org\/10.1016\/j.knosys.2014.04.022. https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705114001440","DOI":"10.1016\/j.knosys.2014.04.022"},{"key":"6_CR20","doi-asserted-by":"publisher","unstructured":"Li, X., Shang, W., Wang, S.: Text-based crude oil price forecasting: a deep learning approach. Int. J. Forecast. 35(4), 1548\u20131560 (2019). https:\/\/doi.org\/10.1016\/j.ijforecast.2018.07.006. https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169207018301110","DOI":"10.1016\/j.ijforecast.2018.07.006"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Liew, J.S.Y.: Fine-grained emotion detection in microblog text. Ph.D. thesis (2016)","DOI":"10.18653\/v1\/N16-2011"},{"issue":"1","key":"6_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-021-00158-4","volume":"9","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Whitfield, C., Zhang, T., Hauser, A., Reynolds, T., Anwar, M.: Monitoring Covid-19 pandemic through the lens of social media using natural language processing and machine learning. Health Inf. Sci. Syst. 9(1), 1\u201316 (2021). https:\/\/doi.org\/10.1007\/s13755-021-00158-4","journal-title":"Health Inf. Sci. Syst."},{"key":"6_CR23","doi-asserted-by":"publisher","unstructured":"Loughran, T., Mcdonald, B.: When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Financ. 66(1), 35\u201365 (2011). https:\/\/doi.org\/10.1111\/j.1540-6261.2010.01625.x","DOI":"10.1111\/j.1540-6261.2010.01625.x"},{"issue":"4","key":"6_CR24","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1111\/1475-679X.12123","volume":"54","author":"T Loughran","year":"2016","unstructured":"Loughran, T., McDonald, B.: Textual analysis in accounting and finance: a survey. J. Account. Res. 54(4), 1187\u20131230 (2016)","journal-title":"J. Account. Res."},{"key":"6_CR25","doi-asserted-by":"publisher","unstructured":"Mahata, A., Rai, A., Nurujjaman, M., Prakash, O., Prasad\u00a0Bal, D.: Characteristics of 2020 stock market crash: the Covid-19 induced extreme event. Chaos: Interdisc. J. Nonlinear Sci. 31(5), 053115 (2021). https:\/\/doi.org\/10.1063\/5.0046704","DOI":"10.1063\/5.0046704"},{"key":"6_CR26","series-title":"The New Palgrave","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/978-1-349-20213-3_13","volume-title":"Finance","author":"BG Malkiel","year":"1989","unstructured":"Malkiel, B.G.: Efficient market hypothesis. In: Eatwell, J., Milgate, M., Newman, P. (eds.) Finance. TNP, pp. 127\u2013134. Springer, Heidelberg (1989). https:\/\/doi.org\/10.1007\/978-1-349-20213-3_13"},{"issue":"4","key":"6_CR27","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1002\/asi.23062","volume":"65","author":"P Malo","year":"2014","unstructured":"Malo, P., Sinha, A., Korhonen, P., Wallenius, J., Takala, P.: Good debt or bad debt: detecting semantic orientations in economic texts: good debt or bad debt. J. Assoc. Inf. Sci. Technol. 65(4), 782\u2013796 (2014). https:\/\/doi.org\/10.1002\/asi.23062","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"6_CR28","doi-asserted-by":"crossref","unstructured":"McCarthy, R.V., McCarthy, M.M., Ceccucci, W., Halawi, L., SpringerLink (Online service): Applying Predictive Analytics Finding Value in Data (2019). oCLC: 1204071994","DOI":"10.1007\/978-3-030-14038-0"},{"key":"6_CR29","doi-asserted-by":"publisher","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). https:\/\/doi.org\/10.48550\/ARXIV.1301.3781","DOI":"10.48550\/ARXIV.1301.3781"},{"key":"6_CR30","unstructured":"OpenAI: OpenAI API. https:\/\/openai.com\/blog\/openai-api. Accessed 20 Sept 2023"},{"key":"6_CR31","doi-asserted-by":"publisher","unstructured":"Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1532\u20131543. Association for Computational Linguistics (2014). https:\/\/doi.org\/10.3115\/v1\/D14-1162","DOI":"10.3115\/v1\/D14-1162"},{"issue":"1","key":"6_CR32","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s10489-006-0001-7","volume":"26","author":"B Qian","year":"2007","unstructured":"Qian, B., Rasheed, K.: Stock market prediction with multiple classifiers. Appl. Intell. 26(1), 25\u201333 (2007)","journal-title":"Appl. Intell."},{"issue":"9","key":"6_CR33","doi-asserted-by":"publisher","first-page":"6279","DOI":"10.1007\/s11042-019-08291-9","volume":"79","author":"D Rousidis","year":"2020","unstructured":"Rousidis, D., Koukaras, P., Tjortjis, C.: Social media prediction: a literature review. Multimed. Tools Appl. 79(9), 6279\u20136311 (2020). https:\/\/doi.org\/10.1007\/s11042-019-08291-9","journal-title":"Multimed. Tools Appl."},{"key":"6_CR34","unstructured":"SciPy: SciPy: Open-source scientific computing library. https:\/\/scipy.org. Accessed 20 Sept 2023"},{"key":"6_CR35","doi-asserted-by":"crossref","unstructured":"Smith, A.: An Inquiry into the Nature and Causes of the Wealth of Nations. McMaster University Archive for the History of Economic Thought (1776). https:\/\/EconPapers.repec.org\/RePEc:hay:hetboo:smith1776","DOI":"10.1093\/oseo\/instance.00043218"},{"key":"6_CR36","unstructured":"Swiss National Science Foundation (SNSF): Bridge. https:\/\/www.snf.ch\/en\/m1BuKkhqcSedG8Ix\/funding\/programmes\/bridge. Accessed 20 Sept 2023"},{"key":"6_CR37","unstructured":"Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298\u20133307. The COLING 2016 Organizing Committee, Osaka, Japan (2016). https:\/\/aclanthology.org\/C16-1311"},{"key":"6_CR38","unstructured":"Vaswani, A., et al.: Attention is all you need. arXiv:1706.03762 [cs] (2017)"},{"issue":"1","key":"6_CR39","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1007\/s12559-021-09839-4","volume":"14","author":"A Weichselbraun","year":"2022","unstructured":"Weichselbraun, A., Steixner, J., Brasoveanu, A.M.P., Scharl, A., G\u00f6bel, M., Nixon, L.J.B.: Automatic expansion of domain-specific affective models for web intelligence applications. Cogn. Comput. 14(1), 228\u2013245 (2022). https:\/\/doi.org\/10.1007\/s12559-021-09839-4","journal-title":"Cogn. Comput."},{"key":"6_CR40","doi-asserted-by":"publisher","unstructured":"Wex, F., Widder, N., Liebmann, M., Neumann, D.: Early warning of impending oil crises using the predictive power of online news stories. In: 2013 46th Hawaii International Conference on System Sciences, Wailea, HI, USA, pp. 1512\u20131521. IEEE (2013). https:\/\/doi.org\/10.1109\/HICSS.2013.186. http:\/\/ieeexplore.ieee.org\/document\/6480021\/","DOI":"10.1109\/HICSS.2013.186"},{"key":"6_CR41","doi-asserted-by":"publisher","unstructured":"Xing, F., Malandri, L., Zhang, Y., Cambria, E.: Financial sentiment analysis: an investigation into common mistakes and silver bullets. In: Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain, pp. 978\u2013987. International Committee on Computational Linguistics (Online) (2020). https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.85. https:\/\/www.aclweb.org\/anthology\/2020.coling-main.85","DOI":"10.18653\/v1\/2020.coling-main.85"},{"key":"6_CR42","unstructured":"Yenicelik, K.D.: Understanding and Exploiting Subspace Organization in Contextual Word Embeddings. Masterthese, Eidgen\u00f6ssische Technische Hochschule Z\u00fcrich, Z\u00fcrich 8006, Schweiz (2020)"}],"container-title":["Lecture Notes in Business Information Processing","Enterprise Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-64748-2_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T07:11:20Z","timestamp":1721891480000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-64748-2_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031647475","9783031647482"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-64748-2_6","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"type":"print","value":"1865-1348"},{"type":"electronic","value":"1865-1356"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"26 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICEIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Enterprise Information Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Prague","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iceis2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iceis.scitevents.org\/?y=2023","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}