{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T23:35:38Z","timestamp":1779060938984,"version":"3.51.4"},"reference-count":71,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T00:00:00Z","timestamp":1638144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In practice, time series forecasting involves the creation of models that generalize data from past values and produce future predictions. Moreover, regarding financial time series forecasting, it can be assumed that the procedure involves phenomena partly shaped by the social environment. Thus, the present work is concerned with the study of the use of sentiment analysis methods in data extracted from social networks and their utilization in multivariate prediction architectures that involve financial data. Through an extensive experimental process, 22 different input setups using such extracted information were tested, over a total of 16 different datasets, under the schemes of 27 different algorithms. The comparisons were structured under two case studies. The first concerns possible improvements in the performance of the forecasts in light of the use of sentiment analysis systems in time series forecasting. The second, having as a framework all the possible versions of the above configuration, concerns the selection of the methods that perform best. The results, as presented by various illustrations, indicate, on the one hand, the conditional improvement of predictability after the use of specific sentiment setups in long-term forecasts and, on the other, a universal predominance of long short-term memory architectures.<\/jats:p>","DOI":"10.3390\/e23121603","type":"journal-article","created":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T22:50:45Z","timestamp":1638226245000},"page":"1603","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Multi-Method Survey on the Use of Sentiment Analysis in Multivariate Financial Time Series Forecasting"],"prefix":"10.3390","volume":"23","author":[{"given":"Charalampos M.","family":"Liapis","sequence":"first","affiliation":[{"name":"Department of Mathematics, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aikaterini","family":"Karanikola","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2247-3082","authenticated-orcid":false,"given":"Sotiris","family":"Kotsiantis","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,29]]},"reference":[{"key":"ref_1","unstructured":"Wei, W.W.S. (2018). Time Series Analysis Univariate and Multivariate Methods, Pearson Addison Wesley."},{"key":"ref_2","first-page":"41","article-title":"Rainfall Forecasting by Technological Machine Learning Models","volume":"200","author":"Hong","year":"2008","journal-title":"Appl. Math. Comput."},{"key":"ref_3","first-page":"19","article-title":"Forecasting Monthly Prices of Gold Using Artificial Neural Network","volume":"9","author":"Chukwudike","year":"2020","journal-title":"J. Stat. Econom. Methods"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, H., and Long, Z. (2020). An Improved Deep Learning Model for Predicting Stock Market Price Time Series. Digit. Signal Process., 102.","DOI":"10.1016\/j.dsp.2020.102741"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liapis, C.M., Karanikola, A., and Kotsiantis, S. (2020). An Ensemble Forecasting Method Using Univariate Time Series COVID-19 Data. ACM Int. Conf. Proc. Ser., 50\u201352.","DOI":"10.1145\/3437120.3437273"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shahid, F., Zameer, A., and Muneeb, M. (2020). Predictions for COVID-19 with Deep Learning Models of LSTM, GRU and Bi-LSTM. Chaos Solit. Fract., 140.","DOI":"10.1016\/j.chaos.2020.110212"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.eswa.2007.09.035","article-title":"Regularized Least Squares Fuzzy Support Vector Regression for Financial Time Series Forecasting","volume":"36","author":"Khemchandani","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/978-3-642-42054-2_75","article-title":"Referential KNN Regression for Financial Time Series Forecasting","volume":"8226","author":"Ban","year":"2013","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.neucom.2018.09.082","article-title":"Time Series Forecasting of Petroleum Production Using Deep LSTM Recurrent Networks","volume":"323","author":"Sagheer","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"180544","DOI":"10.1109\/ACCESS.2020.3028281","article-title":"Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting","volume":"8","author":"Alhussein","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Karanikola, A., Liapis, C.M., and Kotsiantis, S. (2022). A Comparison of Contemporary Methods on Univariate Time Series Forecasting. Advances in Machine Learning\/Deep Learning-Based Technologies, Springer.","DOI":"10.1007\/978-3-030-76794-5_8"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kazmaier, J., and van Vuuren, J.H. (2020). A Generic Framework for Sentiment Analysis: Leveraging Opinion-Bearing Data to Inform Decision Making. Decis. Support Syst., 135.","DOI":"10.1016\/j.dss.2020.113304"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4387","DOI":"10.1007\/s00521-018-3865-7","article-title":"How Textual Quality of Online Reviews Affect Classification Performance: A Case of Deep Learning Sentiment Analysis","volume":"32","author":"Li","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, L., Zhang, L., Xiao, K., and Liu, Q. (2016, January 18\u201321). Forecasting Price Shocks with Social Attention and Sentiment Analysis. Proceedings of the 2016 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, USA.","DOI":"10.1109\/ASONAM.2016.7752291"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"146","DOI":"10.17762\/turcomat.v12i1S.1596","article-title":"Stock Market Increase and Decrease Using Twitter Sentiment Analysis and ARIMA Model","volume":"12","author":"Kedar","year":"2021","journal-title":"Turkish J. Comput. Math. Educ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1002\/for.2616","article-title":"Using Social Media Mining Technology to Improve Stock Price Forecast Accuracy","volume":"39","author":"Huang","year":"2020","journal-title":"J. Forecast."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/cpe.6076","article-title":"Stock Movement Prediction with Sentiment Analysis Based on Deep Learning Networks","volume":"33","author":"Shi","year":"2021","journal-title":"Concurr. Comput."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s12204-017-1818-4","article-title":"Stock Market Forecasting with Financial Micro-Blog Based on Sentiment and Time Series Analysis","volume":"22","author":"Wang","year":"2017","journal-title":"J. Shanghai Jiaotong Univ."},{"key":"ref_20","first-page":"146","article-title":"Sentiment Analysis for Effective Stock Market Prediction","volume":"10","author":"Bharathi","year":"2017","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"ref_21","unstructured":"Barman, A. (2020). Time Series Analysis and Forecasting of COVID-19 Cases Using LSTM and ARIMA Models. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lara-Ben\u00edtez, P., Carranza-Garc\u00eda, M., and Riquelme, J.C. (2021). An Experimental Review on Deep Learning Architectures for Time Series Forecasting. Int. J. Neural Syst., 31.","DOI":"10.1142\/S0129065721300011"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9713","DOI":"10.1007\/s00521-019-04504-2","article-title":"Stock Closing Price Prediction Based on Sentiment Analysis and LSTM","volume":"32","author":"Jin","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1007\/s10586-017-0803-x","article-title":"Model and Forecast Stock Market Behavior Integrating Investor Sentiment Analysis and Transaction Data","volume":"20","author":"Zhang","year":"2017","journal-title":"Cluster Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4","DOI":"10.3389\/fdata.2020.00004","article-title":"AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures","volume":"3","author":"Kaushik","year":"2020","journal-title":"Front. Big Data"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, G., and Guo, J. (2020). A Novel Ensemble Method for Hourly Residential Electricity Consumption Forecasting by Imaging Time Series. Energy, 203.","DOI":"10.1016\/j.energy.2020.117858"},{"key":"ref_27","first-page":"3497","article-title":"Stock Price Prediction Using Combination of LSTM Neural Networks, ARIMA and Sentiment Analysis","volume":"3497","author":"Deorukhkar","year":"2008","journal-title":"Int. Res. J. Eng. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Pasupulety, U., Abdullah Anees, A., Anmol, S., and Mohan, B.R. (2019, January 3\u20135). Predicting Stock Prices Using Ensemble Learning and Sentiment Analysis. Proceedings of the 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Sardinia, Italy.","DOI":"10.1109\/AIKE.2019.00045"},{"key":"ref_29","first-page":"521","article-title":"Use of Machine Learning Algorithms and Twitter Sentiment Analysis for Stock Market Prediction","volume":"115","author":"Pimprikar","year":"2017","journal-title":"Int. J. Pure Appl. Math."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"558","DOI":"10.17148\/IJARCCE.2017.63129","article-title":"Survey: Sentiment Analysis of Twitter Data for Stock Market Prediction","volume":"6","author":"Jadhav","year":"2017","journal-title":"Ijarcce"},{"key":"ref_31","unstructured":"(2021, October 05). Twintproject\/Twint. Available online: https:\/\/github.com\/twintproject\/twint."},{"key":"ref_32","unstructured":"Van Rossum, G. (2020). The Python Library Reference, Release 3.8.2, Python Software Foundation."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bird, S. (2006). NLTK: The Natural Language Toolkit. Proceedings of the COLING\/ACL on Interactive Presentation Sessions, Association for Computational Linguistics.","DOI":"10.3115\/1225403.1225421"},{"key":"ref_34","unstructured":"Bird, S., Klein, E., and Loper, E. (2009). Natural Language Processing with Python, O\u2019Reilly Media."},{"key":"ref_35","unstructured":"(2021, October 05). String\u2014Common String Operations. Available online: https:\/\/docs.python.org\/3\/library\/string.html."},{"key":"ref_36","unstructured":"(2021, October 05). TextBlob: Simplified Text Processing. Available online: https:\/\/textblob.readthedocs.io\/en\/dev\/."},{"key":"ref_37","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":"ICWSM"},{"key":"ref_38","unstructured":"Araci, D. (2021, October 05). FinBERT: Financial Sentiment Analysis with Pre-Trained Language Models. Available online: https:\/\/arxiv.org\/abs\/1908.10063."},{"key":"ref_39","first-page":"4171","article-title":"BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding","volume":"1","author":"Devlin","year":"2019","journal-title":"NAACL HLT 2019-2019 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. Proc. Conf."},{"key":"ref_40","unstructured":"(2021, October 05). ProsusAI\/finBERT. Available online: https:\/\/github.com\/ProsusAI\/finBERT."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1002\/asi.23062","article-title":"Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts","volume":"65","author":"Malo","year":"2014","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"ref_42","unstructured":"Drucker, H. (1997). Improving Regressors Using Boosting Techniques. Proceedings of the Fourteenth International Conference on Machine Learning, Morgan Kaufmann."},{"key":"ref_43","unstructured":"Platt, J., Koller, D., Singer, Y., and Roweis, S. (2008). A New View of Automatic Relevance Determination. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Graves, A., Fern\u00e1ndez, S., and Schmidhuber, J. (2005). Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition. Proceedings of International Conference on Artificial Neural Networks, Springer.","DOI":"10.1007\/11550907_126"},{"key":"ref_45","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_46","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., and Gulin, A. (2019). CatBoost : Unbiased Boosting with Categorical Features. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (2017). Classification and Regression Trees, Routledge.","DOI":"10.1201\/9781315139470"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and Variable Selection via the Elastic Net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely Randomized Trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"ref_50","first-page":"1","article-title":"Xgboost: Extreme Gradient Boosting","volume":"1","author":"Chen","year":"2015","journal-title":"R Packag. Version 0.4-2"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_52","unstructured":"Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., and Stahel, W.A. (2011). Robust Statistics: The Approach Based on Influence Functions, John Wiley & Sons."},{"key":"ref_53","first-page":"1371","article-title":"On the Strong Universal Consistency of Nearest Neighbor Regression Function Estimates","volume":"22","author":"Devroye","year":"2007","journal-title":"Ann. Stat."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., Luo, Z., and Vovk, V. (2013). Kernel Ridge Regression. Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik, Springer.","DOI":"10.1007\/978-3-642-41136-6"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1214\/009053604000000067","article-title":"Least Angle Regression","volume":"32","author":"Efron","year":"2004","journal-title":"Ann. Stat."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression Shrinkage and Selection Via the Lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Fan, J., Ma, X., Wu, L., Zhang, F., Yu, X., and Zeng, W. (2019). Light Gradient Boosting Machine: An Efficient Soft Computing Model for Estimating Daily Reference Evapotranspiration with Local and External Meteorological Data. Agric. Water Manag., 225.","DOI":"10.1016\/j.agwat.2019.105758"},{"key":"ref_58","unstructured":"Seber, G.A.F., and Lee, A.J. (2012). Linear Regression Analysis, John Wiley & Sons."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/0925-2312(91)90023-5","article-title":"Multilayer Perceptrons for Classification and Regression","volume":"2","author":"Murtagh","year":"1991","journal-title":"Neurocomputing"},{"key":"ref_60","unstructured":"Rubinstein, R., Zibulevsky, M., and Elad, M. (2008). Efficient Implementation of the KSVD Algorithm Using Batch Orthogonal Matching Pursuit, Computer Science Department, Technion."},{"key":"ref_61","first-page":"551","article-title":"Online Passive-Aggressive Algorithms","volume":"7","author":"Crammer","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Choi, S., Kim, T., and Yu, W. (2009, January 7\u201310). Performance Evaluation of RANSAC Family. Proceedings of the British Machine Vision Conference, London, UK.","DOI":"10.5244\/C.23.81"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/00031305.1975.10479105","article-title":"Ridge Regression in Practice","volume":"29","author":"Marquardt","year":"1975","journal-title":"Am. Stat."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A Tutorial on Support Vector Regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_66","unstructured":"Dang, X., Peng, H., Wang, X., and Zhang, H. (2021, October 14). The Theil-Sen Estimators in a Multiple Linear Regression Model. Manuscript. Available online: http:\/\/home.olemiss.edu\/~xdang\/pa%0Apers\/."},{"key":"ref_67","unstructured":"(2021, October 12). An Open Source, Low-Code Machine Learning Library in Python. April 2020. Available online: https:\/\/www.pycaret.org."},{"key":"ref_68","unstructured":"(2021, October 12). Keras. GitHub. Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_69","unstructured":"Gulli, A., and Pal, S. (2017). Deep Learning with Keras, Packt Publishing."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","article-title":"The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance","volume":"32","author":"Friedman","year":"1937","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1080\/01621459.1961.10482090","article-title":"Multiple Comparisons Among Means","volume":"56","author":"Dunn","year":"1961","journal-title":"J. Am. Stat. Assoc."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/12\/1603\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:37:30Z","timestamp":1760168250000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/12\/1603"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,29]]},"references-count":71,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["e23121603"],"URL":"https:\/\/doi.org\/10.3390\/e23121603","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,29]]}}}