{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T20:51:51Z","timestamp":1778878311012,"version":"3.51.4"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T00:00:00Z","timestamp":1730160000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T00:00:00Z","timestamp":1730160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001781","name":"Swinburne University of Technology, Australia","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001781","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Predicting stock market behavior using sentiment analysis has become increasingly popular, as customer responses on platforms like Twitter can influence market trends. However, most existing sentiment-based models struggle with two major issues: inaccuracy and high complexity. These problems lead to frequent prediction errors and make the models difficult to implement in real-time trading systems. To address these challenges, this paper proposes a new method called Siagra-ConSA-HSOA (Siamese Graph Convolutional Split-Attention Network with NLP-based Social Sentiment Data). Two data sources feed the model: specifically, NIFTY-50 Stock Market and real-time Twitter sentiment. Through Natural Language Processing (NLP), the raw data is pre-processed and key features are extracted before they are fused into a unified dataset using a cross-domain transformer, namely CDSFT, and then Circle-Inspired Optimization Algorithm (CIOA) selects the most important features from this dataset. This decreases the complexity of the model without losing essential information. Finally, a Graph Convolutional Split-Attention Network (SGCSAN) for promisingly predicting whether the stock prices are going to hit the ground and fly high again or is going to nosedive with Humboldt Squid Optimization Algorithm (HSOA) is introduced to further improve accuracy with lesser error generation. The proposed model Siagra-ConSA-HSOA achieved 99.9% accuracy and 99.8% recall in the testing stage, meaning that such a model performs better than the current approaches both in prediction accuracy and efficiency. Thus, this is a glimmer that the model shall be able to overcome some of the main problems with the current techniques used in predicting the behavior of the stock market.<\/jats:p><jats:p>GitHub Repository: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/jramans2\/Siamese-GCN-SplitAttention-Stock-Prediction.git\">https:\/\/github.com\/jramans2\/Siamese-GCN-SplitAttention-Stock-Prediction.git<\/jats:ext-link><\/jats:p>","DOI":"10.1186\/s40537-024-01016-2","type":"journal-article","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T08:24:49Z","timestamp":1730363089000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions"],"prefix":"10.1186","volume":"11","author":[{"given":"Jayaraman","family":"Kumarappan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elakkiya","family":"Rajasekar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subramaniyaswamy","family":"Vairavasundaram","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ketan","family":"Kotecha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ambarish","family":"Kulkarni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,29]]},"reference":[{"key":"1016_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107994","volume":"239","author":"S Banik","year":"2022","unstructured":"Banik S, Sharma N, Mangla M, Mohanty SN, Shitharth S. LSTM based decision support system for swing trading in stock market. Knowl-Based Syst. 2022;239: 107994.","journal-title":"Knowl-Based Syst"},{"issue":"11","key":"1016_CR2","doi-asserted-by":"publisher","first-page":"12505","DOI":"10.1007\/s10462-023-10442-2","volume":"56","author":"M Bordoloi","year":"2023","unstructured":"Bordoloi M, Biswas SK. Sentiment analysis: a survey on design framework, applications and future scopes. Artif Intell Rev. 2023;56(11):12505\u201360.","journal-title":"Artif Intell Rev"},{"key":"1016_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ribaf.2023.101881","volume":"64","author":"M Costola","year":"2023","unstructured":"Costola M, Hinz O, Nofer M, Pelizzon L. Machine learning sentiment analysis, COVID-19 news and stock market reactions. Res Int Bus Financ. 2023;64: 101881.","journal-title":"Res Int Bus Financ"},{"issue":"1","key":"1016_CR4","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s44196-023-00276-9","volume":"16","author":"JY Huang","year":"2023","unstructured":"Huang JY, Tung CL, Lin WZ. Using social network sentiment analysis and genetic algorithm to improve the stock prediction accuracy of the deep learning-based approach. Int J Comput Intell Syst. 2023;16(1):93.","journal-title":"Int J Comput Intell Syst"},{"key":"1016_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.array.2022.100157","volume":"14","author":"ST Kokab","year":"2022","unstructured":"Kokab ST, Asghar S, Naz S. Transformer-based deep learning models for the sentiment analysis of social media data. Array. 2022;14: 100157.","journal-title":"Array"},{"issue":"4","key":"1016_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksus.2022.101940","volume":"34","author":"MM Akhtar","year":"2022","unstructured":"Akhtar MM, Zamani AS, Khan S, Shatat ASA, Dilshad S, Samdani F. Stock market prediction based on statistical data using machine learning algorithms. J King Saud Univ Sci. 2022;34(4): 101940.","journal-title":"J King Saud Univ Sci"},{"key":"1016_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104908","volume":"113","author":"J Wang","year":"2022","unstructured":"Wang J, Cui Q, Sun X, He M. Asian stock markets closing index forecast based on secondary decomposition, multi-factor analysis and attention-based LSTM model. Eng Appl Artif Intell. 2022;113: 104908.","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"1016_CR8","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1049\/cit2.12052","volume":"7","author":"K Yadav","year":"2022","unstructured":"Yadav K, Yadav M, Saini S. Stock values predictions using deep learning based hybrid models. CAAI Trans Intell Technol. 2022;7(1):107\u201316.","journal-title":"CAAI Trans Intell Technol"},{"issue":"1","key":"1016_CR9","first-page":"4644855","volume":"2022","author":"PN Achyutha","year":"2022","unstructured":"Achyutha PN, Chaudhury S, Bose SC, Kler R, Surve J, Kaliyaperumal K. User classification and stock market-based recommendation engine based on machine learning and Twitter analysis. Math Probl Eng. 2022;2022(1):4644855.","journal-title":"Math Probl Eng"},{"issue":"7","key":"1016_CR10","doi-asserted-by":"publisher","first-page":"5731","DOI":"10.1007\/s10462-022-10144-1","volume":"55","author":"M Wankhade","year":"2022","unstructured":"Wankhade M, Rao ACS, Kulkarni C. A survey on sentiment analysis methods, applications, and challenges. Artif Intell Rev. 2022;55(7):5731\u201380.","journal-title":"Artif Intell Rev"},{"key":"1016_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108781","volume":"247","author":"S Consoli","year":"2022","unstructured":"Consoli S, Barbaglia L, Manzan S. Fine-grained, aspect-based sentiment analysis on economic and financial lexicon. Knowl-Based Syst. 2022;247: 108781.","journal-title":"Knowl-Based Syst"},{"issue":"7","key":"1016_CR12","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","volume":"9","author":"J Ma","year":"2022","unstructured":"Ma J, Tang L, Fan F, Huang J, Mei X, Ma Y. SwinFusion: cross-domain long-range learning for general image fusion via swin transformer. IEEE\/CAA J Autom Sinica. 2022;9(7):1200\u201317.","journal-title":"IEEE\/CAA J Autom Sinica"},{"key":"1016_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.softx.2022.101192","volume":"19","author":"OAP de Souza","year":"2022","unstructured":"de Souza OAP, Miguel LFF. CIOA: circle-inspired optimization Algorithm, an algorithm for engineering optimization. SoftwareX. 2022;19: 101192.","journal-title":"SoftwareX"},{"key":"1016_CR14","doi-asserted-by":"publisher","first-page":"5630117","DOI":"10.1109\/TGRS.2024.3405025","volume":"62","author":"N He","year":"2024","unstructured":"He N, Wang L, Zheng P, Zhang C, Li L. CBSASNet: a siamese network based on channel bias split attention for remote sensing change detection. IEEE Trans Geosci Remote Sens. 2024;62:5630117. https:\/\/doi.org\/10.1109\/TGRS.2024.3405025.","journal-title":"IEEE Trans Geosci Remote Sens."},{"key":"1016_CR15","doi-asserted-by":"publisher","first-page":"122069","DOI":"10.1109\/ACCESS.2023.3328248","volume":"11","author":"MV Anaraki","year":"2023","unstructured":"Anaraki MV, Farzin S. Humboldt Squid Optimization Algorithm (HSOA): a novel nature-inspired technique for solving optimization problems. IEEE Access. 2023;11:122069\u2013115.","journal-title":"IEEE Access"},{"key":"1016_CR16","doi-asserted-by":"publisher","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. 2023;133: 109921.","journal-title":"Appl Soft Comput"},{"key":"1016_CR17","doi-asserted-by":"publisher","first-page":"51353","DOI":"10.1109\/ACCESS.2023.3278790","volume":"11","author":"G Mu","year":"2023","unstructured":"Mu G, Gao N, Wang Y, Dai L. A stock price prediction model based on investor sentiment and optimized deep learning. IEEE Access. 2023;11:51353\u201367.","journal-title":"IEEE Access"},{"issue":"12","key":"1016_CR18","doi-asserted-by":"publisher","first-page":"13675","DOI":"10.1007\/s10489-022-03175-2","volume":"52","author":"T Swathi","year":"2022","unstructured":"Swathi T, Kasiviswanath N, Rao AA. An optimal deep learning-based LSTM for stock price prediction using twitter sentiment analysis. Appl Intell. 2022;52(12):13675\u201388.","journal-title":"Appl Intell"},{"key":"1016_CR19","doi-asserted-by":"publisher","first-page":"35398","DOI":"10.1109\/ACCESS.2022.3163305","volume":"10","author":"R Parekh","year":"2022","unstructured":"Parekh R, Patel NP, Thakkar N, Gupta R, Tanwar S, Sharma G, et al. DL-GuesS: deep learning and sentiment analysis-based cryptocurrency price prediction. IEEE Access. 2022;10:35398\u2013409.","journal-title":"IEEE Access"},{"issue":"1","key":"1016_CR20","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1080\/09540091.2021.1940101","volume":"34","author":"S Wu","year":"2022","unstructured":"Wu S, Liu Y, Zou Z, Weng TH. S_I_LSTM: stock price prediction based on multiple data sources and sentiment analysis. Connect Sci. 2022;34(1):44\u201362.","journal-title":"Connect Sci"},{"key":"1016_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-01839-w","author":"W Khan","year":"2022","unstructured":"Khan W, Ghazanfar MA, Azam MA, Karami A, Alyoubi KH, Alfakeeh AS. Stock market prediction using machine learning classifiers and social media, news. J Ambient Intell Hum Comput. 2022. https:\/\/doi.org\/10.1007\/s12652-020-01839-w.","journal-title":"J Ambient Intell Hum Comput"},{"issue":"2","key":"1016_CR22","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s41060-021-00279-9","volume":"13","author":"Y Li","year":"2022","unstructured":"Li Y, Pan Y. A novel ensemble deep learning model for stock prediction based on stock prices and news. Int J Data Sci Anal. 2022;13(2):139\u201349.","journal-title":"Int J Data Sci Anal"},{"key":"1016_CR23","unstructured":"https:\/\/www.kaggle.com\/datasets\/rohanrao\/nifty50-stock-market-data"},{"key":"1016_CR24","unstructured":"https:\/\/www.kaggle.com\/code\/equinxx\/stock-prediction-gan-twitter-sentiment-analysis"},{"key":"1016_CR25","doi-asserted-by":"publisher","first-page":"1448","DOI":"10.1016\/j.renene.2022.12.036","volume":"202","author":"P Nimmanterdwong","year":"2023","unstructured":"Nimmanterdwong P, Chalermsinsuwan B, Piumsomboon P. Optimizing utilization pathways for biomass to chemicals and energy by integrating emergy analysis and particle swarm optimization (PSO). Renew Energy. 2023;202:1448\u201359.","journal-title":"Renew Energy"},{"key":"1016_CR26","doi-asserted-by":"publisher","first-page":"652","DOI":"10.56294\/sctconf2024652","volume":"3","author":"K Prabavathy","year":"2024","unstructured":"Prabavathy K, Nalini M. Deep learning enabled whale optimization algorithm for accurate prediction of RA disease. Salud, Ciencia y Tecnolog\u00eda-Serie de Conferencias. 2024;3:652\u2013652.","journal-title":"Salud, Ciencia y Tecnolog\u00eda-Serie de Conferencias"},{"issue":"1","key":"1016_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3233\/MGS-230123","volume":"20","author":"TG, H.","year":"2024","unstructured":"TG, H. Femur bone volumetric estimation for osteoporosis classification based on deep learning with tuna jellyfish optimization using X-ray images. Multiagent Grid Syst. 2024;20(1):1\u201325.","journal-title":"Multiagent Grid Syst"},{"key":"1016_CR28","doi-asserted-by":"crossref","unstructured":"Zaman N, Ghazanfar MA, Anwar M, Lee SW, Qazi N, Karimi A, Javed A. Stock market prediction based on machine learning and social sentiment analysis. Authorea Preprints; 2023.","DOI":"10.36227\/techrxiv.22315069"},{"issue":"3","key":"1016_CR29","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1007\/s12559-023-10125-8","volume":"15","author":"Z Wang","year":"2023","unstructured":"Wang Z, Hu Z, Li F, Ho SB, Cambria E. Learning-based stock trending prediction by incorporating technical indicators and social media sentiment. Cogn Comput. 2023;15(3):1092\u2013102.","journal-title":"Cogn Comput"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-024-01016-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-024-01016-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-024-01016-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T08:26:04Z","timestamp":1730363164000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-024-01016-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,29]]},"references-count":29,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1016"],"URL":"https:\/\/doi.org\/10.1186\/s40537-024-01016-2","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,29]]},"assertion":[{"value":"19 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 October 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":"This article does not contain any studies with human participants or animals performed by any of the authors. All the authors involved have agreed to participate in this submitted article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All the authors involved in this manuscript give full consent for publication of this submitted article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"154"}}