PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore
It addresses the problem of limited generalization and scalability in fine-grained sentiment analysis for domains like product reviews and social media, though it appears incremental as it builds on existing prompt learning methods.
The paper tackles fine-grained sentiment analysis by proposing PL-FGSA, a prompt learning framework that integrates prompt design with a TextCNN backbone, achieving F1-scores of 0.922, 0.694, and 0.597 on three benchmark datasets.
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.