{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T18:04:50Z","timestamp":1774721090101,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The volume of data is growing exponentially and becoming more valuable to organizations that collect it, from e-commerce data, shipping, audio and video logs, text messages, internet search queries, stock market activity, financial transactions, the Internet of Things, and various other sources. The major challenges are related with the way to extract insights from such a rich data environment and whether Deep Learning can be successful with Big Data. To get some insight on these topics, social network data are employed as a case study on how sentiments can affect decisions in stock market environments. In this paper, we propose a generalized Deep Learning-based classification framework for Stock Market Sentiment Analysis. This work comprises the study, the development, and implementation of an automatic classification system based on Deep Learning and the validation of its adequacy and efficiency in any scenario, particularly Stock Market Sentiment Analysis. Distinct datasets and several Deep Learning approaches with different layers and embedded techniques are used, and their performances are evaluated. These developments show how Deep Learning reacts to distinct contexts. The results also give context on how different techniques with different parameter combinations react to certain types of data. Convolution obtained the best results when dealing with complex data inputs, and long short-term layers kept a memory of data, allowing inputs which are not as common to still be considered for decisions. The models that resulted from Stock Market Sentiment Analysis datasets were applied with some success to real-life problems. The best models reached accuracies of 73% in training and 69% in certain test datasets. In a simulation, a model was able to provide a Return on Investment of 4.4%. The results contribute to understanding how to process Big Data efficiently using Deep Learning and specialized hardware techniques.<\/jats:p>","DOI":"10.3390\/s22124409","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T02:01:44Z","timestamp":1655085704000},"page":"4409","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Deep Neural Networks Applied to Stock Market Sentiment Analysis"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9581-3973","authenticated-orcid":false,"given":"Filipe","family":"Correia","sequence":"first","affiliation":[{"name":"Institute of Engineering of Porto (ISEP\/P.PORTO), Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida n\\({^\\underline{\\circ}}\\) 431, 4200-072 Porto, Portugal"},{"name":"Interdisciplinary Studies Research Center (ISRC), ISEP\/P.PORTO, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0264-4710","authenticated-orcid":false,"given":"Ana Maria","family":"Madureira","sequence":"additional","affiliation":[{"name":"Institute of Engineering of Porto (ISEP\/P.PORTO), Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida n\\({^\\underline{\\circ}}\\) 431, 4200-072 Porto, Portugal"},{"name":"Interdisciplinary Studies Research Center (ISRC), ISEP\/P.PORTO, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9660-2011","authenticated-orcid":false,"given":"Jorge","family":"Bernardino","sequence":"additional","affiliation":[{"name":"Institute of Engineering of Coimbra (ISEC), Polytechnic of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal"},{"name":"Centre of Informatics and Systems of University of Coimbra-CISUC, Polo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.indmarman.2020.01.005","article-title":"Analytics in the era of big data: The digital transformations and value creation in industrial marketing","volume":"86","author":"Wang","year":"2020","journal-title":"Ind. 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