{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:22:28Z","timestamp":1777890148190,"version":"3.51.4"},"reference-count":31,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["WEB"],"published-print":{"date-parts":[[2024,9,13]]},"abstract":"<jats:p>Stock market forecasting remains a difficult problem in the economics industry due to its incredible stochastic nature. The creation of such an expert system aids investors in making investment decisions about a certain company. Due to the complexity of the stock market, using a single data source is insufficient to accurately reflect all of the variables that influence stock fluctuations. However, predicting stock market movement is a challenging undertaking that requires extensive data analysis, particularly from a big data perspective. In order to address these problems and produce a feasible solution, appropriate statistical models and artificially intelligent algorithms are needed. This paper aims to propose a novel stock market prediction by the following four stages; they are, preprocessing, feature extraction, improved feature level fusion and prediction. The input data is first put through a preparation step in which stock, news, and Twitter data (related to the COVID-19 epidemic) are processed. Under the big data perspective, the input data is taken into account. These pre-processed data are then put through the feature extraction, The improved aspect-based lexicon generation, PMI, and n-gram-based features in this case are derived from the news and Twitter data, while technical indicator-based features are derived from the stock data. The improved feature-level fusion phase is then applied to the extracted features. The ensemble classifiers, which include DBN, CNN, and DRN, were proposed during the prediction phase. Additionally, a SI-MRFO model is suggested to enhance the efficiency of the prediction model by adjusting the best classifier weights. Finally, SI-MRFO model\u2019s effectiveness compared to the existing models with regard to MAE, MAPE, MSE and MSLE. The SI-MRFO accomplished the minimal MAE rate for the 90th learning percentage is approximately 0.015 while other models acquire maximum ratings.<\/jats:p>","DOI":"10.3233\/web-230092","type":"journal-article","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T11:59:57Z","timestamp":1701431997000},"page":"333-357","source":"Crossref","is-referenced-by-count":3,"title":["Stock market prediction-COVID-19 scenario with lexicon-based approach"],"prefix":"10.1177","volume":"22","author":[{"given":"Yalanati","family":"Ayyappa","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.P.","family":"Siva\u00a0Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, JNTUA College of Engineering, Ananthapuramu, Constituent College of Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/WEB-230092_ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-021-04420-6"},{"key":"10.3233\/WEB-230092_ref5","doi-asserted-by":"publisher","DOI":"10.3390\/jrfm15120612"},{"key":"10.3233\/WEB-230092_ref6","doi-asserted-by":"publisher","first-page":"1298","DOI":"10.1007\/s13198-021-01445-9","article-title":"Regional analytics and forecasting for most affected stock markets: The case of GCC stock markets during COVID-19 pandemic","volume":"13","author":"Alkhatib","year":"2022","journal-title":"Int J Syst Assur Eng Manag."},{"key":"10.3233\/WEB-230092_ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-022-01004-5"},{"key":"10.3233\/WEB-230092_ref9","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00430-0"},{"key":"10.3233\/WEB-230092_ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s10614-022-10319-6"},{"key":"10.3233\/WEB-230092_ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-022-00919-3"},{"key":"10.3233\/WEB-230092_ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-022-00919-3"},{"key":"10.3233\/WEB-230092_ref13","doi-asserted-by":"publisher","DOI":"10.1186\/s11782-020-00089-z"},{"key":"10.3233\/WEB-230092_ref14","doi-asserted-by":"crossref","first-page":"13","DOI":"10.47738\/ijiis.v4i1.73","article-title":"Comparison of min-max normalization and Z-score normalization in the K-nearest neighbour (kNN) algorithm to test the accuracy of types of breast cancer","volume":"4","author":"Henderi","year":"2021","journal-title":"Intrnl. Journal of Informatics and Info. Sys."},{"key":"10.3233\/WEB-230092_ref15","doi-asserted-by":"publisher","DOI":"10.1186\/s40854-021-00229-1"},{"key":"10.3233\/WEB-230092_ref16","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Hu, C.\u00a0Zhu and Z.\u00a0Wang, An improved piecewise linear chaotic map based image encryption algorithm, Scientific World Journal (2014).","DOI":"10.1155\/2014\/275818"},{"key":"10.3233\/WEB-230092_ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ESCI50559.2021.9396887"},{"key":"10.3233\/WEB-230092_ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s10660-022-09612-1"},{"key":"10.3233\/WEB-230092_ref19","doi-asserted-by":"crossref","unstructured":"I.\u00a0Namatevs, Deep convolutional neural networks: Structure, feature extraction and training, Information Technology and Management Science 20 (2017).","DOI":"10.1515\/itms-2017-0007"},{"key":"10.3233\/WEB-230092_ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110867"},{"key":"10.3233\/WEB-230092_ref21","doi-asserted-by":"publisher","DOI":"10.1186\/s40854-021-00247-z"},{"key":"10.3233\/WEB-230092_ref22","unstructured":"J.\u00a0Patel, M.\u00a0Patel and M.\u00a0Darji, Stock price prediction using RNN and LSTM, JETIR 5 (2018)."},{"key":"10.3233\/WEB-230092_ref23","doi-asserted-by":"publisher","DOI":"10.1109\/IPEC51340.2021.9421323"},{"key":"10.3233\/WEB-230092_ref24","doi-asserted-by":"publisher","DOI":"10.1186\/s43093-021-00080-x"},{"key":"10.3233\/WEB-230092_ref25","doi-asserted-by":"crossref","unstructured":"T.\u00a0Rajesh Kumar, U.S.B.K.\u00a0Mahalaxmi, M.M.\u00a0Ramakrishna and D.\u00a0Bhatt, Optimization enabled deep residual neural network for motor imagery EEG signal classification, Biomedical Signal Processing and Control 80 (2023).","DOI":"10.1016\/j.bspc.2022.104317"},{"key":"10.3233\/WEB-230092_ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s43546-021-00198-8"},{"key":"10.3233\/WEB-230092_ref27","doi-asserted-by":"publisher","first-page":"3505","DOI":"10.1140\/epjs\/s11734-022-00616-4","article-title":"The changing economic relationship between some of the major COVID-19 impacted countries with prominent wealth: A comparative study from the viewpoint of stock markets","volume":"231","author":"Samadder","year":"2022","journal-title":"Eur. Phys. J. Spec. Top."},{"key":"10.3233\/WEB-230092_ref28","doi-asserted-by":"crossref","unstructured":"X.\u00a0Shao and R.\u00a0Sun, A DBN-based deep neural network model with multitask learning for online air quality prediction, Journal of Comp. Sci. and Eng. (2019).","DOI":"10.1155\/2019\/5304535"},{"key":"10.3233\/WEB-230092_ref29","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-14216-w"},{"key":"10.3233\/WEB-230092_ref30","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1007\/s40745-022-00403-x","article-title":"Global economic impact in stock and commodity markets during COVID-19 pandemic","volume":"9","author":"Sheth","year":"2022","journal-title":"Ann. Data. Sci."},{"issue":"3","key":"10.3233\/WEB-230092_ref31","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1108\/K-06-2021-0457","article-title":"Combined deep learning classifiers for stock market prediction: Integrating stock price and news sentiments","volume":"52","author":"Shilpa","year":"2023","journal-title":"Kybernetes"},{"key":"10.3233\/WEB-230092_ref32","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1007\/s11135-021-01207-6","article-title":"Implementation of the SutteARIMA method to predict short-term cases of the stock market and COVID-19 pandemic in the USA","volume":"56","author":"Singh","year":"2022","journal-title":"Qual. Quant."},{"key":"10.3233\/WEB-230092_ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-023-05178-9"},{"key":"10.3233\/WEB-230092_ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-020-00410-w"},{"key":"10.3233\/WEB-230092_ref35","doi-asserted-by":"crossref","unstructured":"W.\u00a0Wang, Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications, Engineering Applications of Artificial Intelligence 87 (2019), 103300.","DOI":"10.1016\/j.engappai.2019.103300"}],"container-title":["Web Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/WEB-230092","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:27:46Z","timestamp":1777613266000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/WEB-230092"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,13]]},"references-count":31,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/web-230092","relation":{},"ISSN":["2405-6464","2405-6456"],"issn-type":[{"value":"2405-6464","type":"electronic"},{"value":"2405-6456","type":"print"}],"subject":[],"published":{"date-parts":[[2024,9,13]]}}}