{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:48:41Z","timestamp":1784137721846,"version":"3.55.0"},"reference-count":83,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T00:00:00Z","timestamp":1690761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002428","name":"Austrian Science Fund (FWF)","doi-asserted-by":"publisher","award":["DFH-23"],"award-info":[{"award-number":["DFH-23"]}],"id":[{"id":"10.13039\/501100002428","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002428","name":"Austrian Science Fund (FWF)","doi-asserted-by":"publisher","award":["LIT-2020-9-SEE-113"],"award-info":[{"award-number":["LIT-2020-9-SEE-113"]}],"id":[{"id":"10.13039\/501100002428","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State of Upper Austria and the Federal Ministry of Education, Science, and Research","award":["DFH-23"],"award-info":[{"award-number":["DFH-23"]}]},{"name":"State of Upper Austria and the Federal Ministry of Education, Science, and Research","award":["LIT-2020-9-SEE-113"],"award-info":[{"award-number":["LIT-2020-9-SEE-113"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Recently, various methods to predict the future price of financial assets have emerged. One promising approach is to combine the historic price with sentiment scores derived via sentiment analysis techniques. In this article, we focus on predicting the future price of Bitcoin, which is currently the most popular cryptocurrency. More precisely, we propose a hybrid approach, combining time series forecasting and sentiment prediction from microblogs, to predict the intraday price of Bitcoin. Moreover, in addition to standard sentiment analysis methods, we are the first to employ a fine-tuned BERT model for this task. We also introduce a novel weighting scheme in which the weight of the sentiment of each tweet depends on the number of its creator\u2019s followers. For evaluation, we consider periods with strongly varying ranges of Bitcoin prices. This enables us to assess the models w.r.t. robustness and generalization to varied market conditions. Our experiments demonstrate that BERT-based sentiment analysis and the proposed weighting scheme improve upon previous methods. Specifically, our hybrid models that use linear regression as the underlying forecasting algorithm perform best in terms of the mean absolute error (MAE of 2.67) and root mean squared error (RMSE of 3.28). However, more complicated models, particularly long short-term memory networks and temporal convolutional networks, tend to have generalization and overfitting issues, resulting in considerably higher MAE and RMSE scores.<\/jats:p>","DOI":"10.3390\/bdcc7030137","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T09:06:44Z","timestamp":1690880804000},"page":"137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Predicting the Price of Bitcoin Using Sentiment-Enriched Time Series Forecasting"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2404-6096","authenticated-orcid":false,"given":"Markus","family":"Frohmann","sequence":"first","affiliation":[{"name":"Multimedia Mining and Search Group, Institute of Computational Perception, Johannes Kepler University Linz (JKU), 4040 Linz, Austria"},{"name":"Human-Centered AI Group, AI Laboratory, Linz Institute of Technology (LIT), 4040 Linz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manuel","family":"Karner","sequence":"additional","affiliation":[{"name":"Multimedia Mining and Search Group, Institute of Computational Perception, Johannes Kepler University Linz (JKU), 4040 Linz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Said","family":"Khudoyan","sequence":"additional","affiliation":[{"name":"Multimedia Mining and Search Group, Institute of Computational Perception, Johannes Kepler University Linz (JKU), 4040 Linz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Wagner","sequence":"additional","affiliation":[{"name":"Multimedia Mining and Search Group, Institute of Computational Perception, Johannes Kepler University Linz (JKU), 4040 Linz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1706-3406","authenticated-orcid":false,"given":"Markus","family":"Schedl","sequence":"additional","affiliation":[{"name":"Multimedia Mining and Search Group, Institute of Computational Perception, Johannes Kepler University Linz (JKU), 4040 Linz, Austria"},{"name":"Human-Centered AI Group, AI Laboratory, Linz Institute of Technology (LIT), 4040 Linz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"ref_1","unstructured":"Nakamoto, S. (2022, December 01). Bitcoin: A Peer-to-Peer Electronic Cash System. Available online: https:\/\/bitcoin.org\/bitcoin.pdf."},{"key":"ref_2","first-page":"102583","article-title":"A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions","volume":"55","author":"Patel","year":"2020","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_3","first-page":"2","article-title":"To the moon: A history of Bitcoin price manipulation","volume":"13","author":"Peterson","year":"2021","journal-title":"J. Forensic Investig. Account."},{"key":"ref_4","first-page":"1","article-title":"Factors influencing cryptocurrency prices: Evidence from bitcoin, ethereum, dash, litcoin, and monero","volume":"2","author":"Sovbetov","year":"2018","journal-title":"J. Econ. Financ. Anal."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jmoneco.2019.07.002","article-title":"Some simple bitcoin economics","volume":"106","author":"Schilling","year":"2019","journal-title":"J. Monet. Econ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102880","DOI":"10.1016\/j.jimonfin.2023.102880","article-title":"Monetary Policy and Bitcoin","volume":"137","author":"Karau","year":"2023","journal-title":"J. Int. Money Financ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Vuji\u010di\u0107, D., Jagodi\u0107, D., and Ran\u0111i\u0107, S. (2018, January 21\u201323). Blockchain technology, bitcoin and ethereum: A brief overview. Proceedings of the 2018 17th International Symposium INFOTEH-JAHORINA (INFOTEH), East Sarajevo, Bosnia and Herzegovina.","DOI":"10.1109\/INFOTEH.2018.8345547"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1080\/00036846.2015.1109038","article-title":"The economics of BitCoin price formation","volume":"48","author":"Ciaian","year":"2016","journal-title":"Appl. Econ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mudassir, M., Bennbaia, S., and Unal, D. (2020). Time-Series Forecasting of Bitcoin Prices Using High-Dimensional Features: A ML Approach, Springer.","DOI":"10.1007\/s00521-020-05129-6"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pano, T., and Kashef, R. (2020). A Complete VADER-Based Sentiment Analysis of Bitcoin (BTC) Tweets during the Era of COVID-19. Big Data Cogn. Comput., 4.","DOI":"10.3390\/bdcc4040033"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s40854-022-00352-7","article-title":"Bitcoin price change and trend prediction through twitter sentiment and data volume","volume":"8","author":"Gatt","year":"2022","journal-title":"J. Financ. Innov."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pant, D.R., Neupane, P., Poudel, A., Pokhrel, A.K., and Lama, B.K. (2018, January 25\u201327). Recurrent Neural Network Based Bitcoin Price Prediction by Twitter Sentiment Analysis. Proceedings of the 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS), Kathmandu, Nepal.","DOI":"10.1109\/CCCS.2018.8586824"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Serafini, G., Yi, P., Zhang, Q., Brambilla, M., Wang, J., Hu, Y., and Li, B. (2020, January 19\u201324). Sentiment-Driven Price Prediction of the Bitcoin based on Statistical and Deep Learning Approaches. Proceedings of the 2020 International Joint Conference on Neural Networks, IJCNN Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9206704"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e12493","DOI":"10.1111\/exsy.12493","article-title":"Advanced social media sentiment analysis for short-term cryptocurrency price prediction","volume":"37","year":"2020","journal-title":"Expert Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Vyas, P., Vyas, G., and Dhiman, G. (2023). RUemo\u2014The Classification Framework for Russia-Ukraine War-Related Societal Emotions on Twitter through Machine Learning. Algorithms, 16.","DOI":"10.3390\/a16020069"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101824","DOI":"10.1016\/j.compenvurbsys.2022.101824","article-title":"VictimFinder: Harvesting rescue requests in disaster response from social media with BERT","volume":"95","author":"Zhou","year":"2022","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_17","unstructured":"Mateen, M. (2023). Regulation in the Cryptocurrency Industry. [Ph.D. Thesis, University of Missouri\u2013Kansas City]. Available online: https:\/\/mospace.umsystem.edu\/xmlui\/handle\/10355\/95309."},{"key":"ref_18","unstructured":"Fu, S., Wang, Q., Yu, J., and Chen, S. (2022). FTX collapse: A Ponzi story. arXiv."},{"key":"ref_19","unstructured":"Boutsoukis, A. (2023, January 12). Near Real-Time Cryptocurrency Sentiment Analysis. Available online: https:\/\/repository.ihu.edu.gr\/xmlui\/bitstream\/handle\/11544\/30143\/a.boutsoukis_ds.pdf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"109980","DOI":"10.1016\/j.econlet.2021.109980","article-title":"Do investor sentiments drive cryptocurrency prices?","volume":"206","author":"Akyildirim","year":"2021","journal-title":"Econ. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"502295","DOI":"10.3389\/fpsyg.2020.502295","article-title":"Comparison of Psychological Status and Investment Style between Bitcoin Investors and Share Investors","volume":"11","author":"Kim","year":"2020","journal-title":"Front. Psychol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s13278-022-00919-3","article-title":"Effect of public sentiment on stock market movement prediction during the COVID-19 outbreak","volume":"12","author":"Das","year":"2022","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100271","DOI":"10.1016\/j.jbef.2020.100271","article-title":"Herding and anchoring in cryptocurrency markets: Investor reaction to fear and uncertainty","volume":"25","author":"Gurdgiev","year":"2020","journal-title":"J. Behav. Exp. Financ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s13278-021-00776-6","article-title":"A review on sentiment analysis and emotion detection from text","volume":"11","author":"Nandwani","year":"2021","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/S0304-405X(98)00027-0","article-title":"A Model of Investor Sentiment","volume":"49","author":"Barberis","year":"1998","journal-title":"J. Financ. Econ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1609\/icwsm.v8i1.14550","article-title":"VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text","volume":"8","author":"Hutto","year":"2014","journal-title":"Proc. Int. AAAI Conf. Web Soc. Media"},{"key":"ref_27","first-page":"1","article-title":"Evaluating the performance of the most important Lexicons used to Sentiment analysis and opinions Mining","volume":"20","year":"2020","journal-title":"IJCSNS"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.procs.2017.10.118","article-title":"Comparative Evaluation of Sentiment Analysis Methods Across Arabic Dialects","volume":"117","author":"Baly","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_29","first-page":"4171","article-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","volume":"Volume 1","author":"Burstein","year":"2019","journal-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2\u20137 June 2019"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Nguyen, D.Q., Vu, T., and Nguyen, A.T. (2020, January 16\u201320). BERTweet: A pre-trained language model for English Tweets. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Online.","DOI":"10.18653\/v1\/2020.emnlp-demos.2"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"91896","DOI":"10.1109\/ACCESS.2021.3091162","article-title":"Forecast Methods for Time Series Data: A Survey","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.procir.2021.03.088","article-title":"A survey on long short-term memory networks for time series prediction","volume":"99","author":"Lindemann","year":"2021","journal-title":"Procedia Cirp"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1007\/3-540-44668-0_93","article-title":"Applying LSTM to Time Series Predictable through Time-Window Approaches","volume":"Volume 2130","author":"Dorffner","year":"2001","journal-title":"Proceedings of the Artificial Neural Networks\u2014ICANN 2001"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.jfds.2022.12.001","article-title":"Machine learning for cryptocurrency market prediction and trading","volume":"8","author":"Jaquart","year":"2022","journal-title":"J. Financ. Data Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e279","DOI":"10.7717\/peerj-cs.279","article-title":"Forecasting Bitcoin closing price series using linear regression and neural networks models","volume":"6","author":"Uras","year":"2020","journal-title":"Peerj Comput. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"477","DOI":"10.3390\/ai2040030","article-title":"A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms","volume":"2","author":"Hamayel","year":"2021","journal-title":"AI"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dimitriadou, A., and Gregoriou, A. (2023). Predicting Bitcoin Prices Using Machine Learning. Entropy, 25.","DOI":"10.3390\/e25050777"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"107134","DOI":"10.1016\/j.knosys.2021.107134","article-title":"A comprehensive survey on sentiment analysis: Approaches, challenges and trends","volume":"226","author":"Birjali","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"ref_39","first-page":"34","article-title":"Sentiment Analysis Using State of the Art Machine Learning Techniques","volume":"Volume 440","author":"Biele","year":"2022","journal-title":"Proceedings of the Digital Interaction and Machine Intelligence\u2014Proceedings of MIDI\u20192021 - 9th Machine Intelligence and Digital Interaction Conference, Warsaw, Poland, 9\u201310 December 2021"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TTS.2021.3108963","article-title":"Automated Classification of Societal Sentiments on Twitter With Machine Learning","volume":"3","author":"Vyas","year":"2022","journal-title":"IEEE Trans. Technol. Soc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"72879","DOI":"10.1109\/ACCESS.2022.3189670","article-title":"Analyzing Tweeting Patterns and Public Engagement on Twitter During the Recognition Period of the COVID-19 Pandemic: A Study of Two U.S. States","volume":"10","author":"Hoque","year":"2022","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Aslam, N., Xia, K., Rustam, F., Hameed, A., and Ashraf, I. (2022). Using Aspect-Level Sentiments for Calling App Recommendation with Hybrid Deep-Learning Models. Appl. Sci., 12.","DOI":"10.3390\/app12178522"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Balaji, P., and Haritha, D. (2023). An Ensemble Multi-Layered Sentiment Analysis Model (EMLSA) for Classifying the Complex Datasets. Int. J. Adv. Comput. Sci. Appl., 14.","DOI":"10.14569\/IJACSA.2023.0140320"},{"key":"ref_44","first-page":"269","article-title":"textblob Documentation","volume":"2","author":"Loria","year":"2018","journal-title":"Release 0.15"},{"key":"ref_45","first-page":"1097","article-title":"Sentiment analysis: Textblob for decision making","volume":"7","author":"Gujjar","year":"2021","journal-title":"Int. J. Sci. Res. Eng. Trends"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"109780","DOI":"10.1016\/j.knosys.2022.109780","article-title":"Sentiment analysis on Twitter data integrating TextBlob and deep learning models: The case of US airline industry","volume":"255","author":"Aljedaani","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"39313","DOI":"10.1109\/ACCESS.2022.3165621","article-title":"Sentiment Analysis and Emotion Detection on Cryptocurrency Related Tweets Using Ensemble LSTM-GRU Model","volume":"10","author":"Aslam","year":"2022","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e1253","DOI":"10.1002\/widm.1253","article-title":"Deep learning for sentiment analysis: A survey","volume":"8","author":"Zhang","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_49","unstructured":"Dos Santos, C., and Gatti, M. (2014, January 23\u201329). Deep convolutional neural networks for sentiment analysis of short texts. Proceedings of the COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, Dublin, Ireland."},{"key":"ref_50","unstructured":"Zou, Y., Gui, T., Zhang, Q., and Huang, X.J. (2018, January 21\u201325). A lexicon-based supervised attention model for neural sentiment analysis. Proceedings of the 27th International Conference on Computational Linguistics, Santa Fe, NM, USA."},{"key":"ref_51","unstructured":"P\u00e9rez, J.M., Giudici, J.C., and Luque, F. (2021). Pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"13675","DOI":"10.1007\/s10489-022-03175-2","article-title":"An optimal deep learning-based LSTM for stock price prediction using twitter sentiment analysis","volume":"52","author":"Swathi","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Mehtab, S., and Sen, J. (2019). A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing. arXiv.","DOI":"10.2139\/ssrn.3502624"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ye, Z., Wu, Y., Chen, H., Pan, Y., and Jiang, Q. (2022). A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin. Mathematics, 10.","DOI":"10.3390\/math10081307"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Sattarov, O., Jeon, H.S., Oh, R., and Lee, J.D. (2020, January 4\u20136). Forecasting Bitcoin Price Fluctuation by Twitter Sentiment Analysis. Proceedings of the 2020 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, Uzbekistan.","DOI":"10.1109\/ICISCT50599.2020.9351527"},{"key":"ref_56","unstructured":"Bourbakis, N.G., Tsihrintzis, G.A., and Virvou, M. Cryptocurrency Price Prediction using Social Media Sentiment Analysis. Proceedings of the 13th International Conference on Information, Intelligence, Systems & Applications, IISA 2022, Corfu, Greece, 18\u201320 July 2022."},{"key":"ref_57","first-page":"1","article-title":"Cryptocurrency Price Prediction Using Tweet Volumes and Sentiment Analysis","volume":"1","author":"Abraham","year":"2018","journal-title":"Smu Data Sci. Rev."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Mittal, A., Dhiman, V., Singh, A., and Prakash, C. (2019, January 8\u201310). Short-Term Bitcoin Price Fluctuation Prediction Using Social Media and Web Search Data. Proceedings of the 2019 Twelfth International Conference on Contemporary Computing (IC3), Noida, India.","DOI":"10.1109\/IC3.2019.8844899"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Edgari, E., Thiojaya, J., and Qomariyah, N.N. (2022, January 9\u201310). The Impact of Twitter Sentiment Analysis on Bitcoin Price during COVID-19 with XGBoost. Proceedings of the 2022 5th International Conference on Computing and Informatics (ICCI), New Cairo, Cairo, Egypt.","DOI":"10.1109\/ICCI54321.2022.9756123"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Lea, C., Vidal, R., Reiter, A., and Hager, G.D. (15\u201316, January 8\u201310). Temporal Convolutional Networks: A Unified Approach to Action Segmentation. Proceedings of the Computer Vision\u2013ECCV 2016 Workshops, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-49409-8_7"},{"key":"ref_62","unstructured":"Zeng, A., Chen, M., Zhang, L., and Xu, Q. (2022). Are Transformers Effective for Time Series Forecasting?. arXiv."},{"key":"ref_63","first-page":"1","article-title":"Darts: User-Friendly Modern Machine Learning for Time Series","volume":"23","author":"Herzen","year":"2022","journal-title":"J. Mach. Learn. Res."},{"key":"ref_64","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2023, April 09). Automatic Differentiation in Pytorch. Available online: https:\/\/openreview.net\/pdf?id=BJJsrmfCZ."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Ferdiansyah, F., Othman, S.H., Zahilah Raja Md Radzi, R., Stiawan, D., Sazaki, Y., and Ependi, U. (2019, January 2\u20133). A LSTM-Method for Bitcoin Price Prediction: A Case Study Yahoo Finance Stock Market. Proceedings of the 2019 International Conference on Electrical Engineering and Computer Science (ICECOS), Batam, Indonesia.","DOI":"10.1109\/ICECOS47637.2019.8984499"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"113650","DOI":"10.1016\/j.dss.2021.113650","article-title":"Bitcoin price forecasting: A perspective of underlying blockchain transactions","volume":"151","author":"Guo","year":"2021","journal-title":"Decis. Support Syst."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Sharma, A., Bhuriya, D., and Singh, U. (2017, January 20\u201322). Survey of stock market prediction using machine learning approach. Proceedings of the 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India.","DOI":"10.1109\/ICECA.2017.8212715"},{"key":"ref_68","first-page":"2569","article-title":"Social Media and Stock Market Prediction: A Big Data Approach","volume":"67","author":"Awan","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1080\/01605682.2018.1469457","article-title":"Fitting the damped trend method of exponential smoothing","volume":"70","author":"Gardner","year":"2019","journal-title":"J. Oper. Res. Soc."},{"key":"ref_70","first-page":"264","article-title":"The Holt-Winters Forecasting Procedure","volume":"27","author":"Chatfield","year":"1978","journal-title":"J. R. Stat. Soc. Ser. (Appl. Stat.)"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Musbah, H., El-Hawary, M., and Aly, H. (2019, January 16\u201318). Identifying Seasonality in Time Series by Applying Fast Fourier Transform. Proceedings of the 2019 IEEE Electrical Power and Energy Conference (EPEC), Montreal, QC, Canada.","DOI":"10.1109\/EPEC47565.2019.9074776"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/MSPEC.1967.5217220","article-title":"The fast Fourier transform","volume":"4","author":"Brigham","year":"1967","journal-title":"IEEE Spectr."},{"key":"ref_73","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv."},{"key":"ref_74","first-page":"776","article-title":"Linear Regression Analysis Part 14 of a Series on Evaluation of Scientific Publications","volume":"107","author":"Schneider","year":"2010","journal-title":"Dtsch. \u00c4rzteblatt Int."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Ekaputri, A.P., and Akbar, S. (2022, January 28\u201329). Financial News Sentiment Analysis using Modified VADER for Stock Price Prediction. Proceedings of the 2022 9th International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA), Tokoname, Japan.","DOI":"10.1109\/ICAICTA56449.2022.9932925"},{"key":"ref_76","unstructured":"Hutto, C. (2022, November 24). GitHub\u2014cjhutto\/vaderSentiment: VADER Sentiment Analysis. Available online: https:\/\/github.com\/cjhutto\/vaderSentiment."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Nakov, P., Ritter, A., Rosenthal, S., Sebastiani, F., and Stoyanov, V. (2019). SemEval-2016 task 4: Sentiment analysis in Twitter. arXiv.","DOI":"10.18653\/v1\/S16-1001"},{"key":"ref_78","unstructured":"del Arco, F.M.P., Strapparava, C., Lopez, L.A.U., and Mart\u00edn-Valdivia, M.T. (2020, January 11\u201316). EmoEvent: A multilingual emotion corpus based on different events. Proceedings of the 12th Language Resources and Evaluation Conference, Marseille, France."},{"key":"ref_79","unstructured":"Binance (2022, October 15). Binance API. Available online: https:\/\/www.binance.com\/en\/binance-api."},{"key":"ref_80","unstructured":"Kash (2022, November 01). Bitcoin Tweets. Available online: https:\/\/www.kaggle.com\/datasets\/kaushiksuresh147\/bitcoin-tweets."},{"key":"ref_81","unstructured":"Lopez, F. (2022, November 20). Language Detection Library in Python. Available online: https:\/\/github.com\/fedelopez77\/langdetect."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1561\/1500000019","article-title":"The Probabilistic Relevance Framework: BM25 and Beyond","volume":"3","author":"Robertson","year":"2009","journal-title":"Found. Trends Inf. Retr."},{"key":"ref_83","unstructured":"Muralidhar, N., Muthiah, S., Butler, P., Jain, M., Yu, Y., Burne, K., Li, W., Jones, D., Arunachalam, P., and McCormick, H.S. (2021). Using AntiPatterns to avoid MLOps Mistakes. arXiv."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/3\/137\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:23:29Z","timestamp":1760127809000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/3\/137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,31]]},"references-count":83,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["bdcc7030137"],"URL":"https:\/\/doi.org\/10.3390\/bdcc7030137","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,31]]}}}