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Extraction of PE Online Teaching Resources With Positive Psychology Based on Advanced Intelligence Algorithm
Physical education (PE) teaching resources occupy a very important position in the teaching of PE theory. Especially in the context of the Internet era, how to effectively extract PE teaching resources from the Internet is very important for PE teachers. However, the quality of PE teaching resources...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9309176/ https://www.ncbi.nlm.nih.gov/pubmed/35899016 http://dx.doi.org/10.3389/fpsyg.2022.948721 |
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author | Wang, Wei Hu, Juan |
author_facet | Wang, Wei Hu, Juan |
author_sort | Wang, Wei |
collection | PubMed |
description | Physical education (PE) teaching resources occupy a very important position in the teaching of PE theory. Especially in the context of the Internet era, how to effectively extract PE teaching resources from the Internet is very important for PE teachers. However, the quality of PE teaching resources on the Internet is uneven, if not correctly identified, it will bring harm to students’ values. Therefore, it is very necessary to correctly identify the teaching resources of positive psychology. In the era of artificial intelligence, advanced intelligent algorithms provide a solution for the realization of this purpose. In this study, a text sentiment analysis model multi-layer-attention convolutional neural network (ACNN)-CNN based on hierarchical CNN is proposed, which combines the advantages of convolutional neural networks and the attention mechanism. In multi-layer-ACNN-CNN, position encoding information is added to the embedding layer to improve the accuracy of text sentiment classification. In order to verify the performance of the model, online PE teaching resources are extracted by a crawler system and the proposed model is used to classify the positive psychology of the teaching resources. By comparison, the proposed model obtained a better positive psychology classification effect in the experiment, which verifies that the model can extract text features more accurately, and is more suitable for emotion classification of long texts. |
format | Online Article Text |
id | pubmed-9309176 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93091762022-07-26 Extraction of PE Online Teaching Resources With Positive Psychology Based on Advanced Intelligence Algorithm Wang, Wei Hu, Juan Front Psychol Psychology Physical education (PE) teaching resources occupy a very important position in the teaching of PE theory. Especially in the context of the Internet era, how to effectively extract PE teaching resources from the Internet is very important for PE teachers. However, the quality of PE teaching resources on the Internet is uneven, if not correctly identified, it will bring harm to students’ values. Therefore, it is very necessary to correctly identify the teaching resources of positive psychology. In the era of artificial intelligence, advanced intelligent algorithms provide a solution for the realization of this purpose. In this study, a text sentiment analysis model multi-layer-attention convolutional neural network (ACNN)-CNN based on hierarchical CNN is proposed, which combines the advantages of convolutional neural networks and the attention mechanism. In multi-layer-ACNN-CNN, position encoding information is added to the embedding layer to improve the accuracy of text sentiment classification. In order to verify the performance of the model, online PE teaching resources are extracted by a crawler system and the proposed model is used to classify the positive psychology of the teaching resources. By comparison, the proposed model obtained a better positive psychology classification effect in the experiment, which verifies that the model can extract text features more accurately, and is more suitable for emotion classification of long texts. Frontiers Media S.A. 2022-07-08 /pmc/articles/PMC9309176/ /pubmed/35899016 http://dx.doi.org/10.3389/fpsyg.2022.948721 Text en Copyright © 2022 Wang and Hu. 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, Wei Hu, Juan Extraction of PE Online Teaching Resources With Positive Psychology Based on Advanced Intelligence Algorithm |
title | Extraction of PE Online Teaching Resources With Positive Psychology Based on Advanced Intelligence Algorithm |
title_full | Extraction of PE Online Teaching Resources With Positive Psychology Based on Advanced Intelligence Algorithm |
title_fullStr | Extraction of PE Online Teaching Resources With Positive Psychology Based on Advanced Intelligence Algorithm |
title_full_unstemmed | Extraction of PE Online Teaching Resources With Positive Psychology Based on Advanced Intelligence Algorithm |
title_short | Extraction of PE Online Teaching Resources With Positive Psychology Based on Advanced Intelligence Algorithm |
title_sort | extraction of pe online teaching resources with positive psychology based on advanced intelligence algorithm |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9309176/ https://www.ncbi.nlm.nih.gov/pubmed/35899016 http://dx.doi.org/10.3389/fpsyg.2022.948721 |
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