Proposing System to Recognize Emotions in Public Network Using Phobert Deep Learning Model

P. N. Huu, T. Anh, Long Hoang Phi, Dinh Dang Dang, Chau Nguyen Le Bao, Q. Minh
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引用次数: 0

Abstract

People can receive information faster especially in the 4.0 revolution with the continuous development of revolutions. The information can affect our emotional, psychological and spiritual well-being, especially in the recent high school graduation exam across the country. Therefore, we propose to build a user emotion analysis system in a public network with the PhoBert training model in the paper. Besides, we built our dataset aggregated from social networks, articles, blogs, etc. We next use the PhoBert model to solve the processing data problems. The simulation results have shown an accuracy of 86.5% on the training and 81.32% on the validation dataset with a training time of 3 hours (about 180 minutes). The results also show that we can build a warning system to avoid health and psychological effects with great emotion.
基于Phobert深度学习模型的公共网络情绪识别系统
特别是在革命不断发展的4.0革命中,人们可以更快地接收信息。这些信息会影响我们的情感、心理和精神健康,尤其是在最近全国各地的高中毕业考试中。因此,本文提出利用PhoBert训练模型构建公共网络中的用户情感分析系统。此外,我们建立了从社交网络、文章、博客等汇总的数据集。接下来,我们使用PhoBert模型来解决处理数据的问题。仿真结果表明,训练数据集的准确率为86.5%,验证数据集的准确率为81.32%,训练时间为3小时(约180分钟)。结果还表明,我们可以建立一个预警系统,以避免健康和心理影响的情绪。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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