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Efficient recognition of dynamic user emotions based on deep neural networks

The key issue at this stage is how to mine the large amount of valuable user sentiment information from the massive amount of web text and create a suitable dynamic user text sentiment analysis technique. Hence, this study offers a writing feature abstraction process based on ON-LSTM and attention m...

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Autor principal: Zheng, Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9559588/
https://www.ncbi.nlm.nih.gov/pubmed/36247360
http://dx.doi.org/10.3389/fnbot.2022.1006755
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author Zheng, Qi
author_facet Zheng, Qi
author_sort Zheng, Qi
collection PubMed
description The key issue at this stage is how to mine the large amount of valuable user sentiment information from the massive amount of web text and create a suitable dynamic user text sentiment analysis technique. Hence, this study offers a writing feature abstraction process based on ON-LSTM and attention mechanism to address the problem that syntactic information is ignored in emotional text feature extraction. The study found that the Att-ON-LSTM improved the micro-average F1 value by 2.27% and the macro-average F value by 1.7% compared to the Bi-LSTM model with the added attentivity mechanisms. It is demonstrated that it can perform better extraction of semantic information and hierarchical structure information in emotional text and obtain more comprehensive emotional text features. In addition, the ON-LSTM-LS, a sentiment analysis model based on ON-LSTM and tag semantics, is planned to address the problem that tag semantics is ignored in the process of text sentiment analysis. The experimental consequences exposed that the accuracy of the ON-LSTM and labeled semantic sentiment analysis model on the test set is improved by 0.78% with the addition of labeled word directions compared to the model Att-ON-LSTM without the addition of labeled semantic information. The macro-averaged F1 value improved by 1.04%, which indicates that the sentiment analysis process based on ON-LSTM and tag semantics can effectively perform the text sentiment analysis task and improve the sentiment classification effect to some extent. In conclusion, deep learning models for dynamic user sentiment analysis possess high application capabilities.
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spelling pubmed-95595882022-10-14 Efficient recognition of dynamic user emotions based on deep neural networks Zheng, Qi Front Neurorobot Neuroscience The key issue at this stage is how to mine the large amount of valuable user sentiment information from the massive amount of web text and create a suitable dynamic user text sentiment analysis technique. Hence, this study offers a writing feature abstraction process based on ON-LSTM and attention mechanism to address the problem that syntactic information is ignored in emotional text feature extraction. The study found that the Att-ON-LSTM improved the micro-average F1 value by 2.27% and the macro-average F value by 1.7% compared to the Bi-LSTM model with the added attentivity mechanisms. It is demonstrated that it can perform better extraction of semantic information and hierarchical structure information in emotional text and obtain more comprehensive emotional text features. In addition, the ON-LSTM-LS, a sentiment analysis model based on ON-LSTM and tag semantics, is planned to address the problem that tag semantics is ignored in the process of text sentiment analysis. The experimental consequences exposed that the accuracy of the ON-LSTM and labeled semantic sentiment analysis model on the test set is improved by 0.78% with the addition of labeled word directions compared to the model Att-ON-LSTM without the addition of labeled semantic information. The macro-averaged F1 value improved by 1.04%, which indicates that the sentiment analysis process based on ON-LSTM and tag semantics can effectively perform the text sentiment analysis task and improve the sentiment classification effect to some extent. In conclusion, deep learning models for dynamic user sentiment analysis possess high application capabilities. Frontiers Media S.A. 2022-09-29 /pmc/articles/PMC9559588/ /pubmed/36247360 http://dx.doi.org/10.3389/fnbot.2022.1006755 Text en Copyright © 2022 Zheng. 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 Neuroscience
Zheng, Qi
Efficient recognition of dynamic user emotions based on deep neural networks
title Efficient recognition of dynamic user emotions based on deep neural networks
title_full Efficient recognition of dynamic user emotions based on deep neural networks
title_fullStr Efficient recognition of dynamic user emotions based on deep neural networks
title_full_unstemmed Efficient recognition of dynamic user emotions based on deep neural networks
title_short Efficient recognition of dynamic user emotions based on deep neural networks
title_sort efficient recognition of dynamic user emotions based on deep neural networks
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9559588/
https://www.ncbi.nlm.nih.gov/pubmed/36247360
http://dx.doi.org/10.3389/fnbot.2022.1006755
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