Cargando…

A novel text sentiment analysis system using improved depthwise separable convolution neural networks

Human behavior is greatly affected by emotions. Human behavior can be predicted by classifying emotions. Therefore, mining people’s emotional tendencies from text is of great significance for predicting the behavior of target groups and making decisions. The good use of emotion classification techno...

Descripción completa

Detalles Bibliográficos
Autores principales: Kong, Xiaoyu, Zhang, Ke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280403/
https://www.ncbi.nlm.nih.gov/pubmed/37346624
http://dx.doi.org/10.7717/peerj-cs.1236
_version_ 1785060786455969792
author Kong, Xiaoyu
Zhang, Ke
author_facet Kong, Xiaoyu
Zhang, Ke
author_sort Kong, Xiaoyu
collection PubMed
description Human behavior is greatly affected by emotions. Human behavior can be predicted by classifying emotions. Therefore, mining people’s emotional tendencies from text is of great significance for predicting the behavior of target groups and making decisions. The good use of emotion classification technology can produce huge social and economic benefits. However, due to the rapid development of the Internet, the text information generated on the Internet increases rapidly at an unimaginable speed, which makes the previous method of manually classifying texts one-by-one more and more unable to meet the actual needs. In the subject of sentiment analysis, one of the most pressing problems is how to make better use of computer technology to extract emotional tendencies from text data in a way that is both more efficient and accurate. In the realm of text-based sentiment analysis, the currently available deep learning algorithms have two primary issues to contend with. The first is the high level of complexity involved in training the model, and the second is that the model does not take into account all of the aspects of language and does not make use of word vector information. This research employs an upgraded convolutional neural network (CNN) model as a response to these challenges. The goal of this model is to improve the downsides caused by the problems described above. First, the text separable convolution algorithm is used to perform hierarchical convolution on text features to achieve the refined extraction of word vector information and context information. Doing so avoids semantic confusion and reduces the complexity of convolutional networks. Secondly, the text separable convolution algorithm is applied to text sentiment analysis, and an improved CNN is further proposed. Compared with other models, the proposed model shows better performance in text-based sentiment analysis tasks. This study provides great value for text-based sentiment analysis tasks.
format Online
Article
Text
id pubmed-10280403
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher PeerJ Inc.
record_format MEDLINE/PubMed
spelling pubmed-102804032023-06-21 A novel text sentiment analysis system using improved depthwise separable convolution neural networks Kong, Xiaoyu Zhang, Ke PeerJ Comput Sci Bioinformatics Human behavior is greatly affected by emotions. Human behavior can be predicted by classifying emotions. Therefore, mining people’s emotional tendencies from text is of great significance for predicting the behavior of target groups and making decisions. The good use of emotion classification technology can produce huge social and economic benefits. However, due to the rapid development of the Internet, the text information generated on the Internet increases rapidly at an unimaginable speed, which makes the previous method of manually classifying texts one-by-one more and more unable to meet the actual needs. In the subject of sentiment analysis, one of the most pressing problems is how to make better use of computer technology to extract emotional tendencies from text data in a way that is both more efficient and accurate. In the realm of text-based sentiment analysis, the currently available deep learning algorithms have two primary issues to contend with. The first is the high level of complexity involved in training the model, and the second is that the model does not take into account all of the aspects of language and does not make use of word vector information. This research employs an upgraded convolutional neural network (CNN) model as a response to these challenges. The goal of this model is to improve the downsides caused by the problems described above. First, the text separable convolution algorithm is used to perform hierarchical convolution on text features to achieve the refined extraction of word vector information and context information. Doing so avoids semantic confusion and reduces the complexity of convolutional networks. Secondly, the text separable convolution algorithm is applied to text sentiment analysis, and an improved CNN is further proposed. Compared with other models, the proposed model shows better performance in text-based sentiment analysis tasks. This study provides great value for text-based sentiment analysis tasks. PeerJ Inc. 2023-02-15 /pmc/articles/PMC10280403/ /pubmed/37346624 http://dx.doi.org/10.7717/peerj-cs.1236 Text en ©2023 Kong and Zhang https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Kong, Xiaoyu
Zhang, Ke
A novel text sentiment analysis system using improved depthwise separable convolution neural networks
title A novel text sentiment analysis system using improved depthwise separable convolution neural networks
title_full A novel text sentiment analysis system using improved depthwise separable convolution neural networks
title_fullStr A novel text sentiment analysis system using improved depthwise separable convolution neural networks
title_full_unstemmed A novel text sentiment analysis system using improved depthwise separable convolution neural networks
title_short A novel text sentiment analysis system using improved depthwise separable convolution neural networks
title_sort novel text sentiment analysis system using improved depthwise separable convolution neural networks
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280403/
https://www.ncbi.nlm.nih.gov/pubmed/37346624
http://dx.doi.org/10.7717/peerj-cs.1236
work_keys_str_mv AT kongxiaoyu anoveltextsentimentanalysissystemusingimproveddepthwiseseparableconvolutionneuralnetworks
AT zhangke anoveltextsentimentanalysissystemusingimproveddepthwiseseparableconvolutionneuralnetworks
AT kongxiaoyu noveltextsentimentanalysissystemusingimproveddepthwiseseparableconvolutionneuralnetworks
AT zhangke noveltextsentimentanalysissystemusingimproveddepthwiseseparableconvolutionneuralnetworks