{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:15:38Z","timestamp":1758845738435,"version":"3.44.0"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T00:00:00Z","timestamp":1733702400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T00:00:00Z","timestamp":1733702400000},"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":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s41060-024-00692-w","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T06:04:08Z","timestamp":1733724248000},"page":"3737-3758","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Algorithmic trading strategy based on the integration of deep learning models and natural language processing"],"prefix":"10.1007","volume":"20","author":[{"given":"Nesa","family":"Sadeghi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0067-7583","authenticated-orcid":false,"given":"Kamran","family":"Kianfar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nasser","family":"Ghaem Doust","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaber","family":"Fooladi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,9]]},"reference":[{"key":"692_CR1","doi-asserted-by":"crossref","first-page":"108223","DOI":"10.1016\/j.engappai.2024.108223","volume":"133","author":"AK Mishra","year":"2024","unstructured":"Mishra, A.K., Renganathan, J., Gupta, A.: Volatility forecasting and assessing risk of financial markets using multi-transformer neural network based architecture. Eng. Appl. Artif. Intell.Artif. Intell. 133, 108223 (2024)","journal-title":"Eng. Appl. Artif. Intell.Artif. Intell."},{"issue":"2","key":"692_CR2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/S0957-4174(00)00027-0","volume":"19","author":"K-J Kim","year":"2000","unstructured":"Kim, K.-J., Han, I.: Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Syst. Appl. 19(2), 125\u2013132 (2000)","journal-title":"Expert Syst. Appl."},{"issue":"7","key":"692_CR3","doi-asserted-by":"crossref","first-page":"10696","DOI":"10.1016\/j.eswa.2009.02.043","volume":"36","author":"GS Atsalakis","year":"2009","unstructured":"Atsalakis, G.S., Valavanis, K.P.: Forecasting stock market short-term trends using a neuro-fuzzy based methodology. Expert Syst. Appl. 36(7), 10696\u201310707 (2009)","journal-title":"Expert Syst. Appl."},{"issue":"10","key":"692_CR4","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.1109\/TAC.1997.633847","volume":"42","author":"J-SR Jang","year":"1997","unstructured":"Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Trans. Autom. ControlAutom. Control 42(10), 1482\u20131484 (1997)","journal-title":"IEEE Trans. Autom. ControlAutom. Control"},{"issue":"6","key":"692_CR5","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1016\/S0305-0548(02)00037-0","volume":"30","author":"A-S Chen","year":"2003","unstructured":"Chen, A.-S., Leung, M.T., Daouk, H.: Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index. Comput. Oper. Res.. Oper. Res. 30(6), 901\u2013923 (2003)","journal-title":"Comput. Oper. Res.. Oper. Res."},{"key":"692_CR6","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.knosys.2015.01.002","volume":"77","author":"T Xiong","year":"2015","unstructured":"Xiong, T., et al.: A combination method for interval forecasting of agricultural commodity futures prices. Knowl.-Based Syst..-Based Syst. 77, 92\u2013102 (2015)","journal-title":"Knowl.-Based Syst..-Based Syst."},{"issue":"3","key":"692_CR7","doi-asserted-by":"crossref","first-page":"5932","DOI":"10.1016\/j.eswa.2008.07.006","volume":"36","author":"GS Atsalakis","year":"2009","unstructured":"Atsalakis, G.S., Valavanis, K.P.: Surveying stock market forecasting techniques\u2013Part II: soft computing methods. Expert Syst. Appl. 36(3), 5932\u20135941 (2009)","journal-title":"Expert Syst. Appl."},{"key":"692_CR8","doi-asserted-by":"crossref","first-page":"116659","DOI":"10.1016\/j.eswa.2022.116659","volume":"197","author":"MM Kumbure","year":"2022","unstructured":"Kumbure, M.M., et al.: Machine learning techniques and data for stock market forecasting: a literature review. Expert Syst. Appl. 197, 116659 (2022)","journal-title":"Expert Syst. Appl."},{"key":"692_CR9","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.irfa.2014.02.006","volume":"33","author":"C Kearney","year":"2014","unstructured":"Kearney, C., Liu, S.: Textual sentiment in finance: a survey of methods and models. Int. Rev. Financ. Anal.Financ. Anal. 33, 171\u2013185 (2014)","journal-title":"Int. Rev. Financ. Anal.Financ. Anal."},{"key":"692_CR10","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.dss.2016.10.006","volume":"94","author":"SW Chan","year":"2017","unstructured":"Chan, S.W., Chong, M.W.: Sentiment analysis in financial texts. Decis. Support. Syst.. Support Syst. 94, 53\u201364 (2017)","journal-title":"Decis. Support. Syst.. Support Syst."},{"key":"692_CR11","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.knosys.2018.03.004","volume":"150","author":"S Kelly","year":"2018","unstructured":"Kelly, S., Ahmad, K.: Estimating the impact of domain-specific news sentiment on financial assets. Knowl.-Based Syst..-Based Syst. 150, 116\u2013126 (2018)","journal-title":"Knowl.-Based Syst..-Based Syst."},{"issue":"4","key":"692_CR12","first-page":"848","volume":"24","author":"P Koratamaddi","year":"2021","unstructured":"Koratamaddi, P., et al.: Market sentiment-aware deep reinforcement learning approach for stock portfolio allocation. Eng. Sci. Technol. Int. J. 24(4), 848\u2013859 (2021)","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"692_CR13","doi-asserted-by":"crossref","first-page":"109921","DOI":"10.1016\/j.asoc.2022.109921","volume":"133","author":"Y Zhao","year":"2023","unstructured":"Zhao, Y., Yang, G.: Deep learning-based integrated framework for stock price movement prediction. Appl. Soft Comput.Comput. 133, 109921 (2023)","journal-title":"Appl. Soft Comput.Comput."},{"issue":"1","key":"692_CR14","doi-asserted-by":"crossref","first-page":"49","DOI":"10.3905\/jpm.1994.409501","volume":"21","author":"WF Sharpe","year":"1994","unstructured":"Sharpe, W.F.: The sharpe ratio. J. Portf. Manag.Portf. Manag. 21(1), 49\u201358 (1994)","journal-title":"J. Portf. Manag.Portf. Manag."},{"issue":"4","key":"692_CR15","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/S0305-0483(99)00066-3","volume":"28","author":"J Yao","year":"2000","unstructured":"Yao, J., Li, Y., Tan, C.L.: Option price forecasting using neural networks. Omega 28(4), 455\u2013466 (2000)","journal-title":"Omega"},{"issue":"2","key":"692_CR16","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/S0304-3932(01)00042-3","volume":"47","author":"MJ Brennan","year":"2001","unstructured":"Brennan, M.J., Xia, Y.: Stock price volatility and equity premium. J. Monet. Econ. 47(2), 249\u2013283 (2001)","journal-title":"J. Monet. Econ."},{"issue":"5","key":"692_CR17","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/S0165-1889(01)00069-0","volume":"27","author":"M Guidolin","year":"2003","unstructured":"Guidolin, M., Timmermann, A.: Option prices under Bayesian learning: implied volatility dynamics and predictive densities. J. Econ. Dyn. ControlDyn. Control 27(5), 717\u2013769 (2003)","journal-title":"J. Econ. Dyn. ControlDyn. Control"},{"issue":"1","key":"692_CR18","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.ins.2003.03.023","volume":"170","author":"G Armano","year":"2005","unstructured":"Armano, G., Marchesi, M., Murru, A.: A hybrid genetic-neural architecture for stock indexes forecasting. Inf. Sci. 170(1), 3\u201333 (2005)","journal-title":"Inf. Sci."},{"issue":"13\u201315","key":"692_CR19","doi-asserted-by":"crossref","first-page":"3055","DOI":"10.1016\/j.neucom.2009.03.015","volume":"72","author":"X Liang","year":"2009","unstructured":"Liang, X., et al.: Improving option price forecasts with neural networks and support vector regressions. Neurocomputing 72(13\u201315), 3055\u20133065 (2009)","journal-title":"Neurocomputing"},{"issue":"7","key":"692_CR20","doi-asserted-by":"crossref","first-page":"5116","DOI":"10.1016\/j.eswa.2009.12.083","volume":"37","author":"T Ansari","year":"2010","unstructured":"Ansari, T., et al.: Sequential combination of statistics, econometrics and adaptive neural-fuzzy Interface for stock market prediction. Expert Syst. Appl. 37(7), 5116\u20135125 (2010)","journal-title":"Expert Syst. Appl."},{"issue":"7","key":"692_CR21","doi-asserted-by":"crossref","first-page":"8285","DOI":"10.1016\/j.eswa.2011.01.009","volume":"38","author":"H-M Feng","year":"2011","unstructured":"Feng, H.-M., Chou, H.-C.: Evolutional RBFNs prediction systems generation in the applications of financial time series data. Expert Syst. Appl. 38(7), 8285\u20138292 (2011)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"692_CR22","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1016\/j.asoc.2012.08.048","volume":"13","author":"L-Y Wei","year":"2013","unstructured":"Wei, L.-Y.: A GA-weighted ANFIS model based on multiple stock market volatility causality for TAIEX forecasting. Appl. Soft Comput.Comput. 13(2), 911\u2013920 (2013)","journal-title":"Appl. Soft Comput.Comput."},{"issue":"11","key":"692_CR23","doi-asserted-by":"crossref","first-page":"5227","DOI":"10.1016\/j.eswa.2014.01.032","volume":"41","author":"H Park","year":"2014","unstructured":"Park, H., Kim, N., Lee, J.: Parametric models and non-parametric machine learning models for predicting option prices: Empirical comparison study over KOSPI 200 Index options. Expert Syst. Appl. 41(11), 5227\u20135237 (2014)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"692_CR24","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.ejor.2014.07.034","volume":"240","author":"Y Chen","year":"2015","unstructured":"Chen, Y., Wang, X.: A hybrid stock trading system using genetic network programming and mean conditional value-at-risk. Eur. J. Oper. Res.Oper. Res. 240(3), 861\u2013871 (2015)","journal-title":"Eur. J. Oper. Res.Oper. Res."},{"key":"692_CR25","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.eswa.2015.09.029","volume":"44","author":"M G\u00f6\u00e7ken","year":"2016","unstructured":"G\u00f6\u00e7ken, M., et al.: Integrating metaheuristics and artificial neural networks for improved stock price prediction. Expert Syst. Appl. 44, 320\u2013331 (2016)","journal-title":"Expert Syst. Appl."},{"key":"692_CR26","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.eswa.2017.04.030","volume":"83","author":"E Chong","year":"2017","unstructured":"Chong, E., Han, C., Park, F.C.: Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies. Expert Syst. Appl. 83, 187\u2013205 (2017)","journal-title":"Expert Syst. Appl."},{"key":"692_CR27","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.eneco.2017.05.023","volume":"66","author":"Y Zhao","year":"2017","unstructured":"Zhao, Y., Li, J., Yu, L.: A deep learning ensemble approach for crude oil price forecasting. Energy Econ. 66, 9\u201316 (2017)","journal-title":"Energy Econ."},{"key":"692_CR28","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1016\/j.procs.2018.10.340","volume":"143","author":"AJ Balaji","year":"2018","unstructured":"Balaji, A.J., Ram, D.H., Nair, B.B.: Applicability of deep learning models for stock price forecasting an empirical study on BANKEX data. Procedia Comput. Sci. 143, 947\u2013953 (2018)","journal-title":"Procedia Comput. Sci."},{"key":"692_CR29","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.eswa.2018.07.019","volume":"113","author":"Y Baek","year":"2018","unstructured":"Baek, Y., Kim, H.Y.: ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Syst. Appl. 113, 457\u2013480 (2018)","journal-title":"Expert Syst. Appl."},{"key":"692_CR30","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.engappai.2019.07.011","volume":"85","author":"X Fang","year":"2019","unstructured":"Fang, X., Yuan, Z.: Performance enhancing techniques for deep learning models in time series forecasting. Eng. Appl. Artif. Intell.Artif. Intell. 85, 533\u2013542 (2019)","journal-title":"Eng. Appl. Artif. Intell.Artif. Intell."},{"key":"692_CR31","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.eswa.2019.04.038","volume":"132","author":"Y Liu","year":"2019","unstructured":"Liu, Y.: Novel volatility forecasting using deep learning\u2013long short term memory recurrent neural networks. Expert Syst. Appl. 132, 99\u2013109 (2019)","journal-title":"Expert Syst. Appl."},{"key":"692_CR32","doi-asserted-by":"crossref","first-page":"106081","DOI":"10.1016\/j.knosys.2020.106081","volume":"203","author":"P Liu","year":"2020","unstructured":"Liu, P., Liu, J., Wu, K.: CNN-FCM: System modeling promotes stability of deep learning in time series prediction. Knowl.-Based Syst..-Based Syst. 203, 106081 (2020)","journal-title":"Knowl.-Based Syst..-Based Syst."},{"key":"692_CR33","doi-asserted-by":"crossref","first-page":"114332","DOI":"10.1016\/j.eswa.2020.114332","volume":"169","author":"H Rezaei","year":"2021","unstructured":"Rezaei, H., Faaljou, H., Mansourfar, G.: Stock price prediction using deep learning and frequency decomposition. Expert Syst. Appl. 169, 114332 (2021)","journal-title":"Expert Syst. Appl."},{"key":"692_CR34","doi-asserted-by":"crossref","first-page":"106508","DOI":"10.1016\/j.knosys.2020.106508","volume":"211","author":"J Li","year":"2021","unstructured":"Li, J., et al.: DTDR\u2013ALSTM: Extracting dynamic time-delays to reconstruct multivariate data for improving attention-based LSTM industrial time series prediction models. Knowl.-Based Syst..-Based Syst. 211, 106508 (2021)","journal-title":"Knowl.-Based Syst..-Based Syst."},{"key":"692_CR35","doi-asserted-by":"crossref","first-page":"115384","DOI":"10.1016\/j.eswa.2021.115384","volume":"183","author":"L Zheng","year":"2021","unstructured":"Zheng, L., He, H.: Share price prediction of aerospace relevant companies with recurrent neural networks based on pca. Expert Syst. Appl. 183, 115384 (2021)","journal-title":"Expert Syst. Appl."},{"key":"692_CR36","doi-asserted-by":"crossref","first-page":"107836","DOI":"10.1016\/j.asoc.2021.107836","volume":"112","author":"JMD Delgado","year":"2021","unstructured":"Delgado, J.M.D., Oyedele, L.: Deep learning with small datasets: using autoencoders to address limited datasets in construction management. Appl. Soft Comput.Comput. 112, 107836 (2021)","journal-title":"Appl. Soft Comput.Comput."},{"key":"692_CR37","doi-asserted-by":"crossref","first-page":"114632","DOI":"10.1016\/j.eswa.2021.114632","volume":"173","author":"T Th\u00e9ate","year":"2021","unstructured":"Th\u00e9ate, T., Ernst, D.: An application of deep reinforcement learning to algorithmic trading. Expert Syst. Appl. 173, 114632 (2021)","journal-title":"Expert Syst. Appl."},{"key":"692_CR38","doi-asserted-by":"crossref","first-page":"117259","DOI":"10.1016\/j.eswa.2022.117259","volume":"202","author":"LK Felizardo","year":"2022","unstructured":"Felizardo, L.K., et al.: Outperforming algorithmic trading reinforcement learning systems: A supervised approach to the cryptocurrency market. Expert Syst. Appl. 202, 117259 (2022)","journal-title":"Expert Syst. Appl."},{"key":"692_CR39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2022.02.015","volume":"594","author":"AF Kamara","year":"2022","unstructured":"Kamara, A.F., Chen, E., Pan, Z.: An ensemble of a boosted hybrid of deep learning models and technical analysis for forecasting stock prices. Inf. Sci. 594, 1\u201319 (2022)","journal-title":"Inf. Sci."},{"key":"692_CR40","doi-asserted-by":"crossref","first-page":"109876","DOI":"10.1016\/j.asoc.2022.109876","volume":"132","author":"TB \u00c7elik","year":"2023","unstructured":"\u00c7elik, T.B., \u0130can, \u00d6., Bulut, E.: Extending machine learning prediction capabilities by explainable AI in financial time series prediction. Appl. Soft Comput.Comput. 132, 109876 (2023)","journal-title":"Appl. Soft Comput.Comput."},{"key":"692_CR41","doi-asserted-by":"crossref","first-page":"111469","DOI":"10.1016\/j.asoc.2024.111469","volume":"155","author":"T Srivastava","year":"2024","unstructured":"Srivastava, T., Mullick, I., Bedi, J.: Association mining based deep learning approach for financial time-series forecasting. Appl. Soft Comput.Comput. 155, 111469 (2024)","journal-title":"Appl. Soft Comput.Comput."},{"issue":"9","key":"692_CR42","doi-asserted-by":"crossref","first-page":"6409","DOI":"10.1016\/j.eswa.2010.02.078","volume":"37","author":"C-J Huang","year":"2010","unstructured":"Huang, C.-J., et al.: Realization of a news dissemination agent based on weighted association rules and text mining techniques. Expert Syst. Appl. 37(9), 6409\u20136413 (2010)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"692_CR43","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1007\/s00146-020-01111-x","volume":"36","author":"M Vicari","year":"2021","unstructured":"Vicari, M., Gaspari, M.: Analysis of news sentiments using natural language processing and deep learning. AI Soc. 36(3), 931\u2013937 (2021)","journal-title":"AI Soc."},{"issue":"2","key":"692_CR44","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.ipm.2013.12.002","volume":"50","author":"EJ De Fortuny","year":"2014","unstructured":"De Fortuny, E.J., et al.: Evaluating and understanding text-based stock price prediction models. Inf. Process. Manage. 50(2), 426\u2013441 (2014)","journal-title":"Inf. Process. Manage."},{"key":"692_CR45","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.irfa.2016.10.009","volume":"48","author":"A Sun","year":"2016","unstructured":"Sun, A., Lachanski, M., Fabozzi, F.J.: Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction. Int. Rev. Financ. Anal.Financ. Anal. 48, 272\u2013281 (2016)","journal-title":"Int. Rev. Financ. Anal.Financ. Anal."},{"issue":"4","key":"692_CR46","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1016\/j.ijforecast.2018.07.006","volume":"35","author":"X Li","year":"2019","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)","journal-title":"Int. J. Forecast."},{"issue":"4","key":"692_CR47","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1016\/j.ijforecast.2020.05.001","volume":"36","author":"Y Li","year":"2020","unstructured":"Li, Y., et al.: The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning. Int. J. Forecast. 36(4), 1541\u20131562 (2020)","journal-title":"Int. J. Forecast."},{"key":"692_CR48","doi-asserted-by":"crossref","first-page":"108468","DOI":"10.1016\/j.measurement.2020.108468","volume":"168","author":"B Wu","year":"2021","unstructured":"Wu, B., et al.: Effective crude oil price forecasting using new text-based and big-data-driven model. Measurement 168, 108468 (2021)","journal-title":"Measurement"},{"key":"692_CR49","doi-asserted-by":"crossref","first-page":"115568","DOI":"10.1016\/j.eswa.2021.115568","volume":"184","author":"A Hogenboom","year":"2021","unstructured":"Hogenboom, A., Brojba-Micu, A., Frasincar, F.: The impact of word sense disambiguation on stock price prediction. Expert Syst. Appl. 184, 115568 (2021)","journal-title":"Expert Syst. Appl."},{"key":"692_CR50","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.eap.2022.02.010","volume":"74","author":"Q Xie","year":"2022","unstructured":"Xie, Q., et al.: Carbon price prediction considering climate change: a text-based framework. Econ. Anal. Policy 74, 382\u2013401 (2022)","journal-title":"Econ. Anal. Policy"},{"key":"692_CR51","doi-asserted-by":"crossref","first-page":"108742","DOI":"10.1016\/j.knosys.2022.108742","volume":"247","author":"SA Farimani","year":"2022","unstructured":"Farimani, S.A., et al.: Investigating the informativeness of technical indicators and news sentiment in financial market price prediction. Knowl.-Based Syst..-Based Syst. 247, 108742 (2022)","journal-title":"Knowl.-Based Syst..-Based Syst."},{"key":"692_CR52","doi-asserted-by":"crossref","first-page":"109673","DOI":"10.1016\/j.asoc.2022.109673","volume":"130","author":"W-C Lin","year":"2022","unstructured":"Lin, W.-C., Tsai, C.-F., Chen, H.: Factors affecting text mining based stock prediction: text feature representations, machine learning models, and news platforms. Appl. Soft Comput.Comput. 130, 109673 (2022)","journal-title":"Appl. Soft Comput.Comput."},{"key":"692_CR53","doi-asserted-by":"crossref","first-page":"124515","DOI":"10.1016\/j.eswa.2024.124515","volume":"225","author":"S Garc\u00eda-M\u00e9ndez","year":"2024","unstructured":"Garc\u00eda-M\u00e9ndez, S., et al.: Explainable assessment of financial experts\u2019 credibility by classifying social media forecasts and checking the predictions with actual market data. Expert Syst. Appl. 225, 124515 (2024)","journal-title":"Expert Syst. Appl."},{"key":"692_CR54","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.neucom.2014.04.043","volume":"142","author":"X Li","year":"2014","unstructured":"Li, X., et al.: Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information. Neurocomputing 142, 228\u2013238 (2014)","journal-title":"Neurocomputing"},{"issue":"12","key":"692_CR55","doi-asserted-by":"crossref","first-page":"542","DOI":"10.3390\/a16120542","volume":"16","author":"L Belcastro","year":"2023","unstructured":"Belcastro, L., et al.: Enhancing cryptocurrency price forecasting by integrating machine learning with social media and market data. Algorithms 16(12), 542 (2023)","journal-title":"Algorithms"},{"key":"692_CR56","doi-asserted-by":"crossref","first-page":"107788","DOI":"10.1016\/j.asoc.2021.107788","volume":"112","author":"Y-F Chen","year":"2021","unstructured":"Chen, Y.-F., Huang, S.-H.: Sentiment-influenced trading system based on multimodal deep reinforcement learning. Appl. Soft Comput.Comput. 112, 107788 (2021)","journal-title":"Appl. Soft Comput.Comput."},{"key":"692_CR57","doi-asserted-by":"crossref","first-page":"102809","DOI":"10.1016\/j.frl.2022.102809","volume":"46","author":"F Zhang","year":"2022","unstructured":"Zhang, F., Xia, Y.: Carbon price prediction models based on online news information analytics. Financ. Res. Lett.. Res. Lett. 46, 102809 (2022)","journal-title":"Financ. Res. Lett.. Res. Lett."},{"key":"692_CR58","doi-asserted-by":"crossref","first-page":"108712","DOI":"10.1016\/j.knosys.2022.108712","volume":"247","author":"H Xu","year":"2022","unstructured":"Xu, H., Cao, D., Li, S.: A self-regulated generative adversarial network for stock price movement prediction based on the historical price and tweets. Knowl.-Based Syst..-Based Syst. 247, 108712 (2022)","journal-title":"Knowl.-Based Syst..-Based Syst."},{"key":"692_CR59","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1016\/j.procs.2023.01.086","volume":"218","author":"J Maqbool","year":"2023","unstructured":"Maqbool, J., et al.: stock prediction by integrating sentiment scores of financial news and MLP-Regressor: a machine learning approach. Procedia Comput. Sci. 218, 1067\u20131078 (2023)","journal-title":"Procedia Comput. Sci."},{"key":"692_CR60","doi-asserted-by":"crossref","first-page":"122988","DOI":"10.1016\/j.eswa.2023.122988","volume":"245","author":"K Ueda","year":"2024","unstructured":"Ueda, K., et al.: SSCDV: Social media document embedding with sentiment and topics for financial market forecasting. Expert Syst. Appl. 245, 122988 (2024)","journal-title":"Expert Syst. Appl."},{"key":"692_CR61","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.dss.2018.06.008","volume":"112","author":"S Feuerriegel","year":"2018","unstructured":"Feuerriegel, S., Gordon, J.: Long-term stock index forecasting based on text mining of regulatory disclosures. Decis. Support. Syst.. Support Syst. 112, 88\u201397 (2018)","journal-title":"Decis. Support. Syst.. Support Syst."},{"key":"692_CR62","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.procs.2020.06.071","volume":"174","author":"J Yang","year":"2020","unstructured":"Yang, J., et al.: Use GBDT to predict the stock market. Procedia Comput. Sci. 174, 161\u2013171 (2020)","journal-title":"Procedia Comput. Sci."},{"issue":"1","key":"692_CR63","first-page":"100064","volume":"2","author":"NR Naredla","year":"2022","unstructured":"Naredla, N.R., Adedoyin, F.F.: Detection of hyperpartisan news articles using natural language processing technique. Int. J. Inf. Manag. Data Insights 2(1), 100064 (2022)","journal-title":"Int. J. Inf. Manag. Data Insights"},{"issue":"6","key":"692_CR64","first-page":"1120","volume":"21","author":"Z Ozer","year":"2018","unstructured":"Ozer, Z., Ozer, I., Findik, O.: Diacritic restoration of Turkish tweets with word2vec. Eng. Sci. Technol. Int. J. 21(6), 1120\u20131127 (2018)","journal-title":"Eng. Sci. Technol. Int. J."},{"issue":"16\u201318","key":"692_CR65","doi-asserted-by":"crossref","first-page":"3150","DOI":"10.1016\/j.neucom.2008.04.030","volume":"71","author":"C-Y Liou","year":"2008","unstructured":"Liou, C.-Y., Huang, J.-C., Yang, W.-C.: Modeling word perception using the Elman network. Neurocomputing 71(16\u201318), 3150\u20133157 (2008)","journal-title":"Neurocomputing"},{"key":"692_CR66","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/978-3-319-98204-5_17","volume-title":"Engineering applications of neural networks: 19th international conference, EANN 2018, Bristol, UK, September 3-5, 2018, Proceedings","author":"T Wong","year":"2018","unstructured":"Wong, T., Luo, Z.: Recurrent auto-encoder model for large-scale industrial sensor signal analysis. In: Pimenidis, E., Jayne, C. (eds.) Engineering applications of neural networks: 19th international conference, EANN 2018, Bristol, UK, September 3-5, 2018, Proceedings, pp. 203\u2013216. Springer International Publishing, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-98204-5_17"},{"key":"692_CR67","first-page":"102282","volume":"57","author":"H Nguyen","year":"2021","unstructured":"Nguyen, H., et al.: Forecasting and anomaly detection approaches using LSTM and LSTM autoencoder techniques with the applications in supply chain management. Int. J. Inf. Manage. 57, 102282 (2021)","journal-title":"Int. J. Inf. Manage."},{"issue":"7","key":"692_CR68","doi-asserted-by":"crossref","first-page":"e0180944","DOI":"10.1371\/journal.pone.0180944","volume":"12","author":"W Bao","year":"2017","unstructured":"Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE 12(7), e0180944 (2017)","journal-title":"PLoS ONE"},{"key":"692_CR69","volume-title":"Understanding deep learning: application in rare event prediction","author":"C Ranjan","year":"2020","unstructured":"Ranjan, C.: Understanding deep learning: application in rare event prediction. Connaissance Publishing, Atlanta, GA, USA (2020)"},{"issue":"02","key":"692_CR70","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1142\/S0218488598000094","volume":"6","author":"S Hochreiter","year":"1998","unstructured":"Hochreiter, S.: The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 6(02), 107\u2013116 (1998)","journal-title":"Int. J. Uncertain. Fuzziness Knowl. Based Syst."},{"issue":"8","key":"692_CR71","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long Short-term memory. Neural Comput.Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput.Comput."}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-024-00692-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-024-00692-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-024-00692-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T10:53:09Z","timestamp":1758797589000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-024-00692-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,9]]},"references-count":71,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["692"],"URL":"https:\/\/doi.org\/10.1007\/s41060-024-00692-w","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"type":"print","value":"2364-415X"},{"type":"electronic","value":"2364-4168"}],"subject":[],"published":{"date-parts":[[2024,12,9]]},"assertion":[{"value":"11 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}},{"value":"In this study, no human subjects were involved, and no sensitive data or personally identifiable information was collected or analyzed. Therefore, ethical considerations and informed consent procedures for human participants were not applicable to this research.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}]}}