{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:19:41Z","timestamp":1753881581122,"version":"3.41.2"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","funder":[{"name":"the National Key Research and Development Program of China","award":["2018YFB1404103"],"award-info":[{"award-number":["2018YFB1404103"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:p> Humans with the same attribute tend to assume similar emotional and cognitive tendencies. In this study, a machine learning model was constructed to calculate the sample emotion categories\u2019 membership matrices of human groups towards a group of aria samples, thus making a prediction of the human groups\u2019 emotion perception tendencies. Subjects\u2019 binary attributes in terms of gender, family environment and professional background were collected. The subjects were invited to perform an emotion identification of aria segments to build a multi-classification standard dataset of differences in subjects\u2019 emotion identification. A dual-channel neural network model was proposed, and three neural networks based on it were adopted to predict the emotion perception tendencies of the categorical groups with three attributes. Subjects were clustered according to the predicted values of the emotion perception tendencies towards the sample test group, followed by a comparison of the clustering results with the actual attribute group classifications. As suggested by the experimental results, the neural network accurately predicted the classification groups\u2019 emotion perception tendencies with the three attributes of the subjects. The cluster experiment results of subjects, based on the predicted values of the neural networks\u2019 output, had better performance only under the professional background attribute. <\/jats:p>","DOI":"10.1142\/s0218213022500142","type":"journal-article","created":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T10:54:38Z","timestamp":1648724078000},"source":"Crossref","is-referenced-by-count":0,"title":["Prediction Method of Human Group Emotion Perception Tendency Based on a Machine Learning Model"],"prefix":"10.1142","volume":"31","author":[{"given":"Yang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information and Communications Engineering, Communication University of China, Beijing 100024, China"}]},{"given":"Shaobin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Communications Engineering, Communication University of China, Beijing 100024, China"}]},{"given":"Shuchun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Communications Engineering, Communication University of China, Beijing 100024, China"}]},{"given":"Fan","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Information and Communications Engineering, Communication University of China, Beijing 100024, China"}]}],"member":"219","published-online":{"date-parts":[[2022,3,31]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213022500142","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T10:54:42Z","timestamp":1648724082000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213022500142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3]]},"references-count":0,"journal-issue":{"issue":"02","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["10.1142\/S0218213022500142"],"URL":"https:\/\/doi.org\/10.1142\/s0218213022500142","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"type":"print","value":"0218-2130"},{"type":"electronic","value":"1793-6349"}],"subject":[],"published":{"date-parts":[[2022,3]]},"article-number":"2250014"}}