用提示学习统一处理细粒度情感分析，兼顾效果与可解释性。
PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore
- 将细粒度情感分析转为多任务提示生成，融合抽取、分类与解释
- 在三个数据集上达到最高0.922的F1值，低资源下仍表现优异
- 适合需要可解释性与轻量部署的情感分析场景
细粒度情感分析（FGSA）旨在识别文本中特定方面的情感极性，提升产品评论与社交媒体中的意见挖掘精度。传统方法依赖特定架构和大量标注数据，限制了泛化与扩展能力。为此，我们提出基于MindSpore平台的PL-FGSA框架，结合提示设计与轻量TextCNN骨干网络，将FGSA重构为多任务提示增强生成问题，统一处理方面抽取、情感分类与因果解释。通过提示引导，模型增强可解释性，在全量与低资源条件下均表现优异。在SST-2、SemEval-2014 Task 4和MAMS三个基准数据集上的实验表明，模型分别取得0.922、0.694、0.597的F1分数，显著优于传统微调方法，验证了提示学习的泛化有效性及实际应用价值。
原文摘要 · Abstract (English)
Fine-grained sentiment analysis (FGSA) aims to identify sentiment polarity toward specific aspects within a text, enabling more precise opinion mining in domains such as product reviews and social media. However, traditional FGSA approaches often require task-specific architectures and extensive annotated data, limiting their generalization and scalability. To address these challenges, we propose PL-FGSA, a unified prompt learning-based framework implemented using the MindSpore platform, which integrates prompt design with a lightweight TextCNN backbone. Our method reformulates FGSA as a multi-task prompt-augmented generation problem, jointly tackling aspect extraction, sentiment classification, and causal explanation in a unified paradigm. By leveraging prompt-based guidance, PL-FGSA enhances interpretability and achieves strong performance under both full-data and low-resource conditions. Experiments on three benchmark datasets-SST-2, SemEval-2014 Task 4, and MAMS-demonstrate that our model consistently outperforms traditional fine-tuning methods and achieves F1-scores of 0.922, 0.694, and 0.597, respectively. These results validate the effectiveness of prompt-based generalization and highlight the practical value of PL-FGSA for real-world sentiment analysis tasks.
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