Application of an emotional classification model in e-commerce text based on an improved transformer model.
With the rapid development of the mobile internet, people are becoming more dependent on the internet to express their comments on products or stores; meanwhile, text sentiment classification of these comments has become a research hotspot. In existing methods, it is fairly popular to apply a deep l...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0247984 |
| _version_ | 1818824965754978304 |
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| author | Xuyang Wang Yixuan Tong |
| author_facet | Xuyang Wang Yixuan Tong |
| author_sort | Xuyang Wang |
| collection | DOAJ |
| description | With the rapid development of the mobile internet, people are becoming more dependent on the internet to express their comments on products or stores; meanwhile, text sentiment classification of these comments has become a research hotspot. In existing methods, it is fairly popular to apply a deep learning method to the text classification task. Aiming at solving information loss, weak context and other problems, this paper makes an improvement based on the transformer model to reduce the difficulty of model training and training time cost and achieve higher overall model recall and accuracy in text sentiment classification. The transformer model replaces the traditional convolutional neural network (CNN) and the recurrent neural network (RNN) and is fully based on the attention mechanism; therefore, the transformer model effectively improves the training speed and reduces training difficulty. This paper selects e-commerce reviews as research objects and applies deep learning theory. First, the text is preprocessed by word vectorization. Then the IN standardized method and the GELUs activation function are applied based on the original model to analyze the emotional tendencies of online users towards stores or products. The experimental results show that our method improves by 9.71%, 6.05%, 5.58% and 5.12% in terms of recall and approaches the peak level of the F1 value in the test model by comparing BiLSTM, Naive Bayesian Model, the serial BiLSTM_CNN model and BiLSTM with an attention mechanism model. Therefore, this finding proves that our method can be used to improve the text sentiment classification accuracy and effectively apply the method to text classification. |
| first_indexed | 2024-12-19T00:04:15Z |
| format | Article |
| id | doaj.art-39f08940712d455ba94052ef33183339 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-19T00:04:15Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-39f08940712d455ba94052ef331833392022-12-21T20:46:19ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01163e024798410.1371/journal.pone.0247984Application of an emotional classification model in e-commerce text based on an improved transformer model.Xuyang WangYixuan TongWith the rapid development of the mobile internet, people are becoming more dependent on the internet to express their comments on products or stores; meanwhile, text sentiment classification of these comments has become a research hotspot. In existing methods, it is fairly popular to apply a deep learning method to the text classification task. Aiming at solving information loss, weak context and other problems, this paper makes an improvement based on the transformer model to reduce the difficulty of model training and training time cost and achieve higher overall model recall and accuracy in text sentiment classification. The transformer model replaces the traditional convolutional neural network (CNN) and the recurrent neural network (RNN) and is fully based on the attention mechanism; therefore, the transformer model effectively improves the training speed and reduces training difficulty. This paper selects e-commerce reviews as research objects and applies deep learning theory. First, the text is preprocessed by word vectorization. Then the IN standardized method and the GELUs activation function are applied based on the original model to analyze the emotional tendencies of online users towards stores or products. The experimental results show that our method improves by 9.71%, 6.05%, 5.58% and 5.12% in terms of recall and approaches the peak level of the F1 value in the test model by comparing BiLSTM, Naive Bayesian Model, the serial BiLSTM_CNN model and BiLSTM with an attention mechanism model. Therefore, this finding proves that our method can be used to improve the text sentiment classification accuracy and effectively apply the method to text classification.https://doi.org/10.1371/journal.pone.0247984 |
| spellingShingle | Xuyang Wang Yixuan Tong Application of an emotional classification model in e-commerce text based on an improved transformer model. PLoS ONE |
| title | Application of an emotional classification model in e-commerce text based on an improved transformer model. |
| title_full | Application of an emotional classification model in e-commerce text based on an improved transformer model. |
| title_fullStr | Application of an emotional classification model in e-commerce text based on an improved transformer model. |
| title_full_unstemmed | Application of an emotional classification model in e-commerce text based on an improved transformer model. |
| title_short | Application of an emotional classification model in e-commerce text based on an improved transformer model. |
| title_sort | application of an emotional classification model in e commerce text based on an improved transformer model |
| url | https://doi.org/10.1371/journal.pone.0247984 |
| work_keys_str_mv | AT xuyangwang applicationofanemotionalclassificationmodelinecommercetextbasedonanimprovedtransformermodel AT yixuantong applicationofanemotionalclassificationmodelinecommercetextbasedonanimprovedtransformermodel |