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Text Sentiment Analysis Based on Transformer and Augmentation
With the development of Internet technology, social media platforms have become an indispensable part of people’s lives, and social media have been integrated into people’s life, study, and work. On various forums, such as Taobao and Weibo, a large number of people’s footprints are left all the time...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9136405/ https://www.ncbi.nlm.nih.gov/pubmed/35645894 http://dx.doi.org/10.3389/fpsyg.2022.906061 |
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author | Gong, Xiaokang Ying, Wenhao Zhong, Shan Gong, Shengrong |
author_facet | Gong, Xiaokang Ying, Wenhao Zhong, Shan Gong, Shengrong |
author_sort | Gong, Xiaokang |
collection | PubMed |
description | With the development of Internet technology, social media platforms have become an indispensable part of people’s lives, and social media have been integrated into people’s life, study, and work. On various forums, such as Taobao and Weibo, a large number of people’s footprints are left all the time. It is these chats, comments, and other remarks with people’s emotional evaluations that make up part of public opinion. Analysis of this network public opinion is conducive to maintaining the peaceful development of society. Therefore, sentiment analysis has become a hot research field and has made great strides as one of the hot topics in the field of natural language processing. Currently, the BERT model and its variants have achieved excellent results in the field of NLP. However, these models cannot be widely used due to huge demands on computing resources. Therefore, this paper proposes a model based on the transformer mechanism, which mainly includes two parts: knowledge distillation and text augmentation. The former is mainly used to reduce the number of parameters of the model, reducing the computational cost and training time of the model, and the latter is mainly used to expand the task text so that the model can achieve excellent results in the few-sample sentiment analysis task. Experiments show that our model achieves competitive results. |
format | Online Article Text |
id | pubmed-9136405 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91364052022-05-28 Text Sentiment Analysis Based on Transformer and Augmentation Gong, Xiaokang Ying, Wenhao Zhong, Shan Gong, Shengrong Front Psychol Psychology With the development of Internet technology, social media platforms have become an indispensable part of people’s lives, and social media have been integrated into people’s life, study, and work. On various forums, such as Taobao and Weibo, a large number of people’s footprints are left all the time. It is these chats, comments, and other remarks with people’s emotional evaluations that make up part of public opinion. Analysis of this network public opinion is conducive to maintaining the peaceful development of society. Therefore, sentiment analysis has become a hot research field and has made great strides as one of the hot topics in the field of natural language processing. Currently, the BERT model and its variants have achieved excellent results in the field of NLP. However, these models cannot be widely used due to huge demands on computing resources. Therefore, this paper proposes a model based on the transformer mechanism, which mainly includes two parts: knowledge distillation and text augmentation. The former is mainly used to reduce the number of parameters of the model, reducing the computational cost and training time of the model, and the latter is mainly used to expand the task text so that the model can achieve excellent results in the few-sample sentiment analysis task. Experiments show that our model achieves competitive results. Frontiers Media S.A. 2022-05-13 /pmc/articles/PMC9136405/ /pubmed/35645894 http://dx.doi.org/10.3389/fpsyg.2022.906061 Text en Copyright © 2022 Gong, Ying, Zhong and Gong. 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 Gong, Xiaokang Ying, Wenhao Zhong, Shan Gong, Shengrong Text Sentiment Analysis Based on Transformer and Augmentation |
title | Text Sentiment Analysis Based on Transformer and Augmentation |
title_full | Text Sentiment Analysis Based on Transformer and Augmentation |
title_fullStr | Text Sentiment Analysis Based on Transformer and Augmentation |
title_full_unstemmed | Text Sentiment Analysis Based on Transformer and Augmentation |
title_short | Text Sentiment Analysis Based on Transformer and Augmentation |
title_sort | text sentiment analysis based on transformer and augmentation |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9136405/ https://www.ncbi.nlm.nih.gov/pubmed/35645894 http://dx.doi.org/10.3389/fpsyg.2022.906061 |
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