Triple-Cascade Prompt-Based Implicit Sentiment Analysis with Data Augmentation and Automated Feedback Correction
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To address the challenges of implicit aspect-based sentiment analysis, specifically the lack of explicit sentiment expressions and the complexity of text semantics, we introduce a three-stage cascaded prompting reasoning model for implicit sentiment analysis based on data augmentation and automatic feedback correction. Initially, the model enhances the semantic information of the text by extracting target word concept representations from external knowledge bases, while also leveraging syntactic desensitization transformations to enhance syntactic information. Subsequently, we construct prompt templates and concatenate them with the enhanced text, inputting the result into a T5 model to infer target aspect words, implicit opinion expressions, and sentiment polarity in a stepwise manner. Finally, the model employs a large language model to self-correct the inference results, further improving the accuracy of the analysis. Experimental results demonstrate that the proposed model achieves F1-scores of 74.63% and 76.17% on the Restaurant and Laptop datasets, respectively. Compared to mainstream implicit aspect-based sentiment analysis models, this represents an improvement of 2.35% and 0.58%. These findings validate the effectiveness of the proposed model in the implicit aspect-based sentiment analysis task.
Abstract
To address the challenges of implicit aspect-based sentiment analysis, specifically the lack of explicit sentiment expressions and the complexity of text semantics, we introduce a three-stage cascaded prompting reasoning model for implicit sentiment analysis based on data augmentation and automatic feedback correction. Initially, the model enhances the semantic information of the text by extracting target word concept representations from external knowledge bases, while also leveraging syntactic desensitization transformations to enhance syntactic information. Subsequently, we construct prompt templates and concatenate them with the enhanced text, inputting the result into a T5 model to infer target aspect words, implicit opinion expressions, and sentiment polarity in a stepwise manner. Finally, the model employs a large language model to self-correct the inference results, further improving the accuracy of the analysis. Experimental results demonstrate that the proposed model achieves F1-scores of 74.63% and 76.17% on the Restaurant and Laptop datasets, respectively. Compared to mainstream implicit aspect-based sentiment analysis models, this represents an improvement of 2.35% and 0.58%. These findings validate the effectiveness of the proposed model in the implicit aspect-based sentiment analysis task.