Abstract
The earlier prediction of stock based on sentiment analysis is an essential factor in the finance-based intersection. However, the stock details prediction based on sentiment classification is not easy due to stock dataset variations; predicting stock details in an earlier stage requires good functions. Hence, the presented article has developed a novel Strawberry-based Bi-directional Recurrent Neural Model (SBRNM) to efficiently analyze the sentiments for stock prediction. The Twitter dataset is collected, and the preprocessing, feature selection, and sentiment analysis processes are done. The sentiments are classified with the help of strawberry fitness. Furthermore, the designed procedure is executed in the python environment with appropriate packages. Consequently, based on the sentiments, the stock details are predicted. The robustness of the projected method was verified by measuring the metrics like precision, F-measure, accuracy, execution time, error rate, and recall. Moreover, the presented model has attained an accuracy of 97.06%. In addition, to determine the proposed framework's effectiveness, SBRNM was compared with existing methods, and it has gained better results than other models.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdul Sattar K, Obeidat Q, Akour M (2020) Towards harnessing based learning algorithms for tweets sentiment analysis. 2020 International conference on innovation and intelligence for informatics, computing and technologies (3ICT), IEEE https://doi.org/10.1109/3ICT51146.2020.9311990
Balaji SN, Paul PV, Saravanan R (2017) Survey on sentiment analysis based stock prediction using big data analytics. 2017 Innovations in power and advanced computing technologies (i-PACT), pp. 1–5 https://doi.org/10.1109/IPACT.2017.8244943
Bustos O, Pomares-Quimbaya A (2020) Stock market movement forecast: a systematic review. Expert Syst Appl 156:113464. https://doi.org/10.1016/j.eswa.2020.113464
Cavalcante RC, Brasileiro RC, Souza VLF et al (2016) Computational intelligence and financial markets: a survey and future directions. Expert Syst Appl 55:194–211. https://doi.org/10.1016/j.eswa.2016.02.006
Chen W, Zhang H, Mehlawat MK, Jia L (2021) Mean–variance portfolio optimization using machine learning-based stock price prediction. Appl Soft Comput 100:106943. https://doi.org/10.1016/j.asoc.2020.106943
Choi J, Yoon J, Chung J, Coh BY, Lee JM (2020) Social media analytics and business intelligence research: a systematic review. Inf Process Manage 57(6):102279. https://doi.org/10.1016/j.ipm.2020.102279
de Pauli STZ, Kleina M, Bonat WH (2020) Comparing artificial neural network architectures for Brazilian stock market prediction. Ann Data Sci 7(4):613–628. https://doi.org/10.1007/s40745-020-00305-w
Dhyani M, Kumar R (2021) An intelligent Chatbot using deep learning with bi-directional RNN and attention model. Mater Today Proc 34:817–824. https://doi.org/10.1016/j.matpr.2020.05.450
Ferreira FGDC, Gandomi AH, Cardoso RTN (2021) Artificial intelligence applied to stock market trading: a review. IEEE Access 9:30898–30917. https://doi.org/10.1109/ACCESS.2021.3058133
Hamraoui I, Boubaker A (2022) Impact of Twitter sentiment on stock price returns. SocNetw Anal Min 12:28. https://doi.org/10.1007/s13278-021-00856-7
Hoseinzade E, Haratizadeh S (2019) CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Syst Appl 129:273–285. https://doi.org/10.1016/j.eswa.2019.03.029
Iqbal R, Doctor F, More B et al (2020) Big data analytics: computational intelligence techniques and application areas. Technol Forecast Soc Change 153:119253. https://doi.org/10.1016/j.techfore.2018.03.024
Jing N, Wu Z, Wang H (2021) A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction. Expert SystAppl 178:115019. https://doi.org/10.1016/j.eswa.2021.115019
Ketsetsis AP, Kourounis C, Spanos G et al. (2020) Deep learning techniques for stock market prediction in the European union: a systematic review. 2020 International conference on computational science and computational intelligence (CSCI), IEEE https://doi.org/10.1109/CSCI51800.2020.00107
Kilimci ZH, Duvar R (2020) An efficient word embedding and deep learning based model to forecast the direction of stock exchange market using twitter and financial news sites: a case of istanbul stock exchange (BIST 100). IEEE Access 8:188186–188198. https://doi.org/10.1109/ACCESS.2020.3029860
Kumar D, Sarangi PK, Verma R (2021) A systematic review of stock market prediction using machine learning and statistical techniques. Mater Today: Proc, Elsevier, Amsterdam https://doi.org/10.1016/j.matpr.2020.11.399
Li AW, Bastos GS (2020) Stock market forecasting using deep learning and technical analysis: a systematic review. IEEE Access 8:185232–185242. https://doi.org/10.1109/ACCESS.2020.3030226
Moghar A, Hamiche M (2020) Stock market prediction using LSTM recurrent neural network. Procedia Comput Sci 170:1168–1173. https://doi.org/10.1016/j.procs.2020.03.049
Misra M, Yadav AP, Kaur H (2018) Stock market prediction using machine learning algorithms: a classification study. 2018 International conference on recent innovations in electrical, electronics and communication engineering (ICRIEECE), pp. 2475–2478 https://doi.org/10.1109/ICRIEECE44171.2018.9009178
Nabipour M, Nayyeri P, Jabani H, Shahab S, Mosavi A (2020) Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. IEEE Access 8:150199–150212. https://doi.org/10.1109/ACCESS.2020.3015966
Nagamanjula R, Pethalakshmi A (2020) A novel framework based on bi-objective optimization and LAN2FIS for Twitter sentiment analysis. SocNetw Anal Min 10:34. https://doi.org/10.1007/s13278-020-00648-5
Ni H, Wang S, Cheng P (2021) A hybrid approach for stock trend prediction based on tweets embedding and historical prices. World Wide Web 24(3):849–868. https://doi.org/10.1007/s11280-021-00880-9
Orimoloye LO, Sung M-C, Ma T, Johnson JEV (2020) Comparing the effectiveness of deep feedforward neural networks and shallow architectures for predicting stock price indices. Expert Syst Appl 139:112828. https://doi.org/10.1016/j.eswa.2019.112828
Pang X, Zhou Y, Wang P, Lin W, Chang V (2020) An innovative neural network approach for stock market prediction. J Supercomput 76:2098–2118. https://doi.org/10.1007/s11227-017-2228-y
Rani S, Kumar P (2019) Deep learning based sentiment analysis using convolution neural network. Arab J SciEng 44(4):3305–3314. https://doi.org/10.1007/s13369-018-3500-z
Rao PS, Srinivas K, Mohan AK (2020) A survey on stock market prediction using machine learning techniques. ICDSMLA 2019, Springer, Singapore, pp. 923–931 https://doi.org/10.1007/978-981-15-1420-3_101
Saurabh S, Dey K (2020) Unraveling the relationship between social moods and the stock market: evidence from the United Kingdom. J Behav Exp Finance 26:100300. https://doi.org/10.1016/j.jbef.2020.100300
Sert OC, Şahin SD, Özyer T, Alhajj R (2020) Analysis and prediction in sparse and high dimensional text data: the case of Dow Jones stock market. Phys a: Stat MechAppl 545:123752. https://doi.org/10.1016/j.physa.2019.123752
Sóti A, Ancarani A, Stéger J et al (2020) Influence of Twitter activity on the stock price of soccer clubs. SocNetw Anal Min 10:79. https://doi.org/10.1007/s13278-020-00691-2
Sreejith S, Nehemiah HK, Kannan A (2020) A classification framework using a diverse intensified strawberry optimized neural network (DISON) for clinical decision-making. Cogn Syst Res 64:98–116. https://doi.org/10.1016/j.cogsys.2020.08.003
Vijh M, Chandola D, Tikkiwal VA, Kumar A (2020) Stock closing price prediction using machine learning techniques. Procedia Comput Sci 167:599–606. https://doi.org/10.1016/j.procs.2020.03.326
Xie X, Ge S, Hu F, Xie M, Jiang N (2019) An improved algorithm for sentiment analysis based on maximum entropy. Soft Comput 23(2):599–611. https://doi.org/10.1007/s00500-017-2904-0
Yadav R, Kumar AV, Kumar A (2019) News-based supervised sentiment analysis for prediction of futures buying behaviour. IIMB Manag Rev 31(2):157–166. https://doi.org/10.1016/j.iimb.2019.03.006
Zavadzki S, Kleina M, Drozda F, Marques M (2020) Computational intelligence techniques used for stock market prediction: a systematic review. IEEE Lat Am Trans 18(04):744–755. https://doi.org/10.1109/TLA.2020.9082218
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interests
The authors declare that they have no potential conflict of interest.
Ethical approval
All applicable institutional and/or national guidelines for the care and use of animals were followed.
Informed consent
For this type of analysis formal consent is not needed.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Pattewar, T., Jain, D. Stock prediction analysis by customers opinion in Twitter data using an optimized intelligent model. Soc. Netw. Anal. Min. 12, 152 (2022). https://doi.org/10.1007/s13278-022-00979-5
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13278-022-00979-5