https://doi.org/10.1140/epjs/s11734-024-01412-y
Regular Article
Machine learning methods for emotion recognition in neuromarketing tasks
Financial University Under the Government of the Russian Federation, 49/2 Leningradsky Ave., 125167, Moscow, Russia
Received:
29
October
2024
Accepted:
14
November
2024
Published online:
10
December
2024
The paper analyzes various machine learning algorithms, including SVM, ANN, CNN, and popular libraries, focusing on their effectiveness in emotion classification tasks. A software package based on the proposed CNN has been developed, allowing one to determine human emotions with an accuracy of over 90%. Using the developed software package and the proposed machine learning model, an experiment was conducted on three large banks to evaluate advertising campaigns for issuing credit cards. Based on practical examples, it is demonstrated how emotion recognition can optimize marketing strategies, improve customer interactions, and increase loyalty.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024
Springer Nature or its licensor (e.g. a society or other partner) 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.
