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Emotion Recognition Algorithm Application Financial Development and Economic Growth Status and Development Trend

Financial market and economic growth and development trends can be regarded as an extremely complex system, and the in-depth study and prediction of this complex system has always been the focus of attention of economists and other scholars. Emotion recognition algorithm is a pattern recognition tec...

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Detalles Bibliográficos
Autores principales: Wang, Dahai, Li, Bing, Yan, Xuebo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8918688/
https://www.ncbi.nlm.nih.gov/pubmed/35295376
http://dx.doi.org/10.3389/fpsyg.2022.856409
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author Wang, Dahai
Li, Bing
Yan, Xuebo
author_facet Wang, Dahai
Li, Bing
Yan, Xuebo
author_sort Wang, Dahai
collection PubMed
description Financial market and economic growth and development trends can be regarded as an extremely complex system, and the in-depth study and prediction of this complex system has always been the focus of attention of economists and other scholars. Emotion recognition algorithm is a pattern recognition technology that integrates a number of emerging science and technology, and has good non-linear system fitting capabilities. However, using emotion recognition algorithm models to analyze and predict financial market and economic growth and development trends can yield more accurate prediction results. This article first gives a detailed introduction to the existing financial development and economic growth status and development trend forecasting problems, and then gives a brief overview of the concept of emotion recognition algorithms. Then, it describes the emotion recognition methods, including statistical emotion recognition methods, mixed emotion recognition methods, and emotion recognition methods based on knowledge technology, and conducts in-depth research on the three algorithm models of statistical emotion recognition methods, they are the support vector machine algorithm model, the artificial neural network algorithm model, and the long and short-term memory network algorithm model. Finally, these three algorithm models are applied to the financial market and economic growth and development trend prediction experiments. Experimental results show that the average absolute error of the three algorithms is below 25, which verifies that the emotion recognition algorithm has good operability and feasibility for the prediction of financial market and economic growth and development trends.
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spelling pubmed-89186882022-03-15 Emotion Recognition Algorithm Application Financial Development and Economic Growth Status and Development Trend Wang, Dahai Li, Bing Yan, Xuebo Front Psychol Psychology Financial market and economic growth and development trends can be regarded as an extremely complex system, and the in-depth study and prediction of this complex system has always been the focus of attention of economists and other scholars. Emotion recognition algorithm is a pattern recognition technology that integrates a number of emerging science and technology, and has good non-linear system fitting capabilities. However, using emotion recognition algorithm models to analyze and predict financial market and economic growth and development trends can yield more accurate prediction results. This article first gives a detailed introduction to the existing financial development and economic growth status and development trend forecasting problems, and then gives a brief overview of the concept of emotion recognition algorithms. Then, it describes the emotion recognition methods, including statistical emotion recognition methods, mixed emotion recognition methods, and emotion recognition methods based on knowledge technology, and conducts in-depth research on the three algorithm models of statistical emotion recognition methods, they are the support vector machine algorithm model, the artificial neural network algorithm model, and the long and short-term memory network algorithm model. Finally, these three algorithm models are applied to the financial market and economic growth and development trend prediction experiments. Experimental results show that the average absolute error of the three algorithms is below 25, which verifies that the emotion recognition algorithm has good operability and feasibility for the prediction of financial market and economic growth and development trends. Frontiers Media S.A. 2022-02-28 /pmc/articles/PMC8918688/ /pubmed/35295376 http://dx.doi.org/10.3389/fpsyg.2022.856409 Text en Copyright © 2022 Wang, Li and Yan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Wang, Dahai
Li, Bing
Yan, Xuebo
Emotion Recognition Algorithm Application Financial Development and Economic Growth Status and Development Trend
title Emotion Recognition Algorithm Application Financial Development and Economic Growth Status and Development Trend
title_full Emotion Recognition Algorithm Application Financial Development and Economic Growth Status and Development Trend
title_fullStr Emotion Recognition Algorithm Application Financial Development and Economic Growth Status and Development Trend
title_full_unstemmed Emotion Recognition Algorithm Application Financial Development and Economic Growth Status and Development Trend
title_short Emotion Recognition Algorithm Application Financial Development and Economic Growth Status and Development Trend
title_sort emotion recognition algorithm application financial development and economic growth status and development trend
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8918688/
https://www.ncbi.nlm.nih.gov/pubmed/35295376
http://dx.doi.org/10.3389/fpsyg.2022.856409
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