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Imbalanced Text Sentiment Classification Based on Multi-Channel BLTCN-BLSTM Self-Attention
With the continuous expansion of the field of natural language processing, researchers have found that there is a phenomenon of imbalanced data distribution in some practical problems, and the excellent performance of most methods is based on the assumption that the samples in the dataset are data b...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967901/ https://www.ncbi.nlm.nih.gov/pubmed/36850855 http://dx.doi.org/10.3390/s23042257 |
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author | Cai, Tiantian Zhang, Xinsheng |
author_facet | Cai, Tiantian Zhang, Xinsheng |
author_sort | Cai, Tiantian |
collection | PubMed |
description | With the continuous expansion of the field of natural language processing, researchers have found that there is a phenomenon of imbalanced data distribution in some practical problems, and the excellent performance of most methods is based on the assumption that the samples in the dataset are data balanced. Therefore, the imbalanced data classification problem has gradually become a problem that needs to be studied. Aiming at the sentiment information mining of an imbalanced short text review dataset, this paper proposed a fusion multi-channel BLTCN-BLSTM self-attention sentiment classification method. By building a multi-channel BLTCN-BLSTM self-attention network model, the sample after word embedding processing is used as the input of the multi-channel, and after fully extracting features, the self-attention mechanism is fused to strengthen the sentiment to further fully extract text features. At the same time, focus loss rebalancing and classifier enhancement are combined to realize text sentiment predictions. The experimental results show that the optimal F1 value is up to 0.893 on the Chnsenticorp-HPL-10,000 corpus. The comparison and ablation of experimental results, including accuracy, recall, and F1-measure, show that the proposed model can fully integrate the weight of emotional feature words. It effectively improves the sentiment classification performance of imbalanced short-text review data. |
format | Online Article Text |
id | pubmed-9967901 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99679012023-02-27 Imbalanced Text Sentiment Classification Based on Multi-Channel BLTCN-BLSTM Self-Attention Cai, Tiantian Zhang, Xinsheng Sensors (Basel) Article With the continuous expansion of the field of natural language processing, researchers have found that there is a phenomenon of imbalanced data distribution in some practical problems, and the excellent performance of most methods is based on the assumption that the samples in the dataset are data balanced. Therefore, the imbalanced data classification problem has gradually become a problem that needs to be studied. Aiming at the sentiment information mining of an imbalanced short text review dataset, this paper proposed a fusion multi-channel BLTCN-BLSTM self-attention sentiment classification method. By building a multi-channel BLTCN-BLSTM self-attention network model, the sample after word embedding processing is used as the input of the multi-channel, and after fully extracting features, the self-attention mechanism is fused to strengthen the sentiment to further fully extract text features. At the same time, focus loss rebalancing and classifier enhancement are combined to realize text sentiment predictions. The experimental results show that the optimal F1 value is up to 0.893 on the Chnsenticorp-HPL-10,000 corpus. The comparison and ablation of experimental results, including accuracy, recall, and F1-measure, show that the proposed model can fully integrate the weight of emotional feature words. It effectively improves the sentiment classification performance of imbalanced short-text review data. MDPI 2023-02-17 /pmc/articles/PMC9967901/ /pubmed/36850855 http://dx.doi.org/10.3390/s23042257 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cai, Tiantian Zhang, Xinsheng Imbalanced Text Sentiment Classification Based on Multi-Channel BLTCN-BLSTM Self-Attention |
title | Imbalanced Text Sentiment Classification Based on Multi-Channel BLTCN-BLSTM Self-Attention |
title_full | Imbalanced Text Sentiment Classification Based on Multi-Channel BLTCN-BLSTM Self-Attention |
title_fullStr | Imbalanced Text Sentiment Classification Based on Multi-Channel BLTCN-BLSTM Self-Attention |
title_full_unstemmed | Imbalanced Text Sentiment Classification Based on Multi-Channel BLTCN-BLSTM Self-Attention |
title_short | Imbalanced Text Sentiment Classification Based on Multi-Channel BLTCN-BLSTM Self-Attention |
title_sort | imbalanced text sentiment classification based on multi-channel bltcn-blstm self-attention |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9967901/ https://www.ncbi.nlm.nih.gov/pubmed/36850855 http://dx.doi.org/10.3390/s23042257 |
work_keys_str_mv | AT caitiantian imbalancedtextsentimentclassificationbasedonmultichannelbltcnblstmselfattention AT zhangxinsheng imbalancedtextsentimentclassificationbasedonmultichannelbltcnblstmselfattention |