{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T18:33:26Z","timestamp":1771353206452,"version":"3.50.1"},"reference-count":19,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T00:00:00Z","timestamp":1630368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Digital"],"abstract":"<jats:p>The topic of affective computing has been growing rapidly in recent times. In the last five years, the volume of publications in this field has tripled. The question arises which research trends are most in demand today. This can only be judged by analysing the publications that present the results of research. Since researchers have access to the entire global scientific publication space, the task of analysing big data arises. This leads to the problem of identifying the most significant results in the subject area of interest. This paper presents some results of the analysis of semi-structured information from scientific citation databases on the subject of \u201caffective computing\u201d.<\/jats:p>","DOI":"10.3390\/digital1030012","type":"journal-article","created":{"date-parts":[[2021,8,31]],"date-time":"2021-08-31T22:58:15Z","timestamp":1630450695000},"page":"162-172","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Extracting Information on Affective Computing Research from Data Analysis of Known Digital Platforms: Research into Emotional Artificial Intelligence"],"prefix":"10.3390","volume":"1","author":[{"given":"Nafissa","family":"Yusupova","sequence":"first","affiliation":[{"name":"Department of Computer Science and Robotics, Ufa State Aviation Technical University, 450008 Ufa, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diana","family":"Bogdanova","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Robotics, Ufa State Aviation Technical University, 450008 Ufa, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2568-6179","authenticated-orcid":false,"given":"Nadejda","family":"Komendantova","sequence":"additional","affiliation":[{"name":"International Institute for Advanced Systems Analysis (IIASA), A-2361 Laxenburg, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0897-8663","authenticated-orcid":false,"given":"Hossein","family":"Hassani","sequence":"additional","affiliation":[{"name":"Research Institute of Energy Management and Planning, University of Tehran, Tehran 1417466191, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yusupova, N., Smetanina, O., Gayanova, M., and Komendantova, N. (2021). Semi-structured information in the field of artificial intelligence and information security: Processing results. IOP Conf. Series Mater. Sci. Eng., 1069.","DOI":"10.1088\/1757-899X\/1069\/1\/012012"},{"key":"ref_2","unstructured":"Tomkins, S.S. (1962). Affect Imagery Consciousness: The Positive Affects, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Plutchik, R. (1980). A General Psychoevolutionary Theory of Emotion: Theories Emotion, Elsevier.","DOI":"10.1016\/B978-0-12-558701-3.50007-7"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.dss.2018.09.002","article-title":"Deep learning for affective computing: Text-based emotion recognition in decision support","volume":"115","author":"Kratzwald","year":"2018","journal-title":"Decis. Support Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1016\/j.im.2015.02.003","article-title":"Emotion recognition and affective computing on vocal social media","volume":"52","author":"Dai","year":"2015","journal-title":"Inf. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.procs.2015.07.314","article-title":"EEG-based Subject Independent Affective Computing Models","volume":"53","author":"Bozhkov","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.inffus.2017.02.003","article-title":"A review of affective computing: From unimodal analysis to multi-modal fusion","volume":"37","author":"Poria","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.neucom.2020.02.085","article-title":"Wavelet packet analysis for speaker-independent emotion recognition","volume":"398","author":"Wang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Halim, Z., Waqar, M., and Tahir, M. (2020). A machine learning-based investigation utilizing the in-text features for the identi-fication of dominant emotion in an email. Knowl.-Based Syst., 208.","DOI":"10.1016\/j.knosys.2020.106443"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.cogsys.2019.10.005","article-title":"Facial expressions and subjective assessments of emotions","volume":"59","author":"Vartanov","year":"2019","journal-title":"Cogn. Syst. Res."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhu, L., Su, C., Zhang, J., Cui, G., Cichocki, A., Zhou, C., and Li, J. (2020). EEG-based approach for recognizing human social emotion perception. Adv. Eng. Inform., 46.","DOI":"10.1016\/j.aei.2020.101191"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, X. (2020). Examining the role of emotion in online consumer reviews of various attributes in the surprise box shopping model. Decis. Support Syst., 136.","DOI":"10.1016\/j.dss.2020.113344"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.patrec.2020.11.009","article-title":"Speech emotion recognition model based on Bi-GRU and Focal Loss","volume":"140","author":"Zhu","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1016\/j.procs.2019.09.145","article-title":"Evaluation of Student Information System (SIS) In Terms of User Emotion, Performance and Perceived Usability: A Turkish University Case (An Empirical Study)","volume":"158","author":"Demirkol","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kazmaier, J., and van Vuuren, J. (2020). A generic framework for sentiment analysis: Leveraging opinion-bearing data to in-form decision making. Decis. Support Syst., 135.","DOI":"10.1016\/j.dss.2020.113304"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.future.2020.06.019","article-title":"Ontology-driven aspect-based sentiment analysis classifica-tion: An infodemiological case study regarding infectious diseases in Latin America","volume":"112","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chiarello, F., Bonaccorsi, A., and Fantoni, G. (2020). Technical Sentiment Analysis. Measuring Advantages and Drawbacks of New Products Using Social Media. Comput. Ind., 123.","DOI":"10.1016\/j.compind.2020.103299"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lin, H., Wang, T., Lin, G., Cheng, S., Chen, H., and Huang, Y. (2020). Applying sentiment analysis to automatically classify con-sumer comments concerning marketing 4Cs aspects. Appl. Soft Comput., 97.","DOI":"10.1016\/j.asoc.2020.106755"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.future.2020.06.050","article-title":"Transformer based Deep Intelligent Contextual Embedding for Twitter sentiment analysis","volume":"113","author":"Naseem","year":"2020","journal-title":"Futur. Gener. Comput. Syst."}],"container-title":["Digital"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2673-6470\/1\/3\/12\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:54:03Z","timestamp":1760165643000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2673-6470\/1\/3\/12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,31]]},"references-count":19,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["digital1030012"],"URL":"https:\/\/doi.org\/10.3390\/digital1030012","relation":{},"ISSN":["2673-6470"],"issn-type":[{"value":"2673-6470","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,31]]}}}