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...
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| Vydáno v: | PeerJ. Computer science Ročník 9; s. e1236 |
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| Jazyk: | angličtina |
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15.02.2023
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| ISSN: | 2376-5992, 2376-5992 |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| ArticleNumber | e1236 |
| Audience | Academic |
| Author | Zhang, Ke Kong, Xiaoyu |
| Author_xml | – sequence: 1 givenname: Xiaoyu surname: Kong fullname: Kong, Xiaoyu organization: Wuxi Vocational Institute of Commerce, Wuxi, Jiangsu, China – sequence: 2 givenname: Ke surname: Zhang fullname: Zhang, Ke organization: Wuxi Vocational Institute of Commerce, Wuxi, Jiangsu, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37346624$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1515_biol_2022_0859 crossref_primary_10_1080_03081079_2025_2456960 |
| Cites_doi | 10.36660/abc.20200596 10.48550/arXiv.1408.5882 10.1109/MCG.2021.3115387 10.1007/s11277-017-5144-9 10.1016/j.cie.2018.06.034 10.1016/j.eswa.2008.07.035 10.1007/s10791-008-9070-z 10.9728/dcs.2019.20.7.1429 10.2174/0929866527666201103145635 10.1016/j.ipm.2014.05.001 10.1109/TLA.2022.9661475 10.1161/CIRCIMAGING.121.012838 10.1007/s11042-020-10314-9 10.1177/07435584211006920 10.1016/j.knosys.2016.02.011 10.1007/s12559-016-9386-8 10.1109/ACCESS.2021.3094925 10.1109/TMM.2020.2985526 10.1007/s11042-018-5870-3 10.1007/s40617-020-00445-8 |
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| Copyright | 2023 Kong and Zhang. COPYRIGHT 2023 PeerJ. Ltd. 2023 Kong and Zhang. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), 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. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023 Kong and Zhang 2023 Kong and Zhang |
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| Keywords | Emotion analysis system Depthwise separable convolution Convolution neural network Text information |
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| SubjectTerms | Accuracy Algorithms Artificial neural networks Bioinformatics Classification College students Complexity Convolution neural network Data mining Data Mining and Machine Learning Deep learning Depthwise separable convolution Dictionaries Economic development Emotion analysis system Emotions Feature extraction Human acts Human behavior Human-Computer Interaction Internet Machine learning Neural Networks Sentiment Analysis Technology application Technology utilization Text information Words (language) |
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| Title | A novel text sentiment analysis system using improved depthwise separable convolution neural networks |
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