A Dual-Template Prompted Mutual Learning Generative Model for Implicit Aspect-Based Sentiment Analysis
Generative models have shown excellent results in aspect-based sentiment analysis tasks by predicting quadruples by setting specific template formats. The existing research predicts sentiment elements and enhances the dependency between elements using the multi-template prompting method, but it does...
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MDPI AG
2024-09-01
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| author | Zhou Lei Yawei Zhang Shengbo Chen |
| author_facet | Zhou Lei Yawei Zhang Shengbo Chen |
| author_sort | Zhou Lei |
| collection | DOAJ |
| description | Generative models have shown excellent results in aspect-based sentiment analysis tasks by predicting quadruples by setting specific template formats. The existing research predicts sentiment elements and enhances the dependency between elements using the multi-template prompting method, but it does not realize the information interaction in the generation process, and it ignores the dependency between the prompt template and the aspect terms and opinion terms in the input sequence. In this paper, we propose a Dual-template Prompted Mutual Learning (DPML) generative model to enhance the information interaction between generation modules. Specifically, this paper designs a dual template based on prompt learning and, at the same time, develops a mutual learning information enhancement module to guide each generated training process to interact with iterative information. Secondly, in the decoding stage, a label marking the interactive learning module is added to share the explicit emotional expression in the sequence, which can enhance the ability of the model to capture implicit emotion. On two public datasets, our model achieves an average improvement of 5.3% and 3.4% in F1 score compared with the previous state-of-the-art model. In the implicit sentiment analysis experiment, the F1 score of the proposed model in the data subset containing implicit words is increased by 2.75% and 3.42%, respectively. |
| format | Article |
| id | doaj-art-9ce148c7aac847baab334a04d833fa48 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-9ce148c7aac847baab334a04d833fa482025-08-20T01:47:41ZengMDPI AGApplied Sciences2076-34172024-09-011419871910.3390/app14198719A Dual-Template Prompted Mutual Learning Generative Model for Implicit Aspect-Based Sentiment AnalysisZhou Lei0Yawei Zhang1Shengbo Chen2School of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaGenerative models have shown excellent results in aspect-based sentiment analysis tasks by predicting quadruples by setting specific template formats. The existing research predicts sentiment elements and enhances the dependency between elements using the multi-template prompting method, but it does not realize the information interaction in the generation process, and it ignores the dependency between the prompt template and the aspect terms and opinion terms in the input sequence. In this paper, we propose a Dual-template Prompted Mutual Learning (DPML) generative model to enhance the information interaction between generation modules. Specifically, this paper designs a dual template based on prompt learning and, at the same time, develops a mutual learning information enhancement module to guide each generated training process to interact with iterative information. Secondly, in the decoding stage, a label marking the interactive learning module is added to share the explicit emotional expression in the sequence, which can enhance the ability of the model to capture implicit emotion. On two public datasets, our model achieves an average improvement of 5.3% and 3.4% in F1 score compared with the previous state-of-the-art model. In the implicit sentiment analysis experiment, the F1 score of the proposed model in the data subset containing implicit words is increased by 2.75% and 3.42%, respectively.https://www.mdpi.com/2076-3417/14/19/8719implicit sentiment analysisprompt learningmutual learning |
| spellingShingle | Zhou Lei Yawei Zhang Shengbo Chen A Dual-Template Prompted Mutual Learning Generative Model for Implicit Aspect-Based Sentiment Analysis Applied Sciences implicit sentiment analysis prompt learning mutual learning |
| title | A Dual-Template Prompted Mutual Learning Generative Model for Implicit Aspect-Based Sentiment Analysis |
| title_full | A Dual-Template Prompted Mutual Learning Generative Model for Implicit Aspect-Based Sentiment Analysis |
| title_fullStr | A Dual-Template Prompted Mutual Learning Generative Model for Implicit Aspect-Based Sentiment Analysis |
| title_full_unstemmed | A Dual-Template Prompted Mutual Learning Generative Model for Implicit Aspect-Based Sentiment Analysis |
| title_short | A Dual-Template Prompted Mutual Learning Generative Model for Implicit Aspect-Based Sentiment Analysis |
| title_sort | dual template prompted mutual learning generative model for implicit aspect based sentiment analysis |
| topic | implicit sentiment analysis prompt learning mutual learning |
| url | https://www.mdpi.com/2076-3417/14/19/8719 |
| work_keys_str_mv | AT zhoulei adualtemplatepromptedmutuallearninggenerativemodelforimplicitaspectbasedsentimentanalysis AT yaweizhang adualtemplatepromptedmutuallearninggenerativemodelforimplicitaspectbasedsentimentanalysis AT shengbochen adualtemplatepromptedmutuallearninggenerativemodelforimplicitaspectbasedsentimentanalysis AT zhoulei dualtemplatepromptedmutuallearninggenerativemodelforimplicitaspectbasedsentimentanalysis AT yaweizhang dualtemplatepromptedmutuallearninggenerativemodelforimplicitaspectbasedsentimentanalysis AT shengbochen dualtemplatepromptedmutuallearninggenerativemodelforimplicitaspectbasedsentimentanalysis |