用多头注意力和TF-IDF优化LSTM，提升情感分析准确率12%。
Advancing Sentiment Analysis: A Novel LSTM Framework with Multi-head Attention
- 结合TF-IDF与多头注意力增强LSTM特征提取能力。
- 测试集准确率达80.28%，较标准LSTM提升12%。
- 适合舆情监控、推荐系统等实际应用场景。
本文提出一种基于LSTM的情感分类模型，融合多头注意力机制与TF-IDF优化。通过引入TF-IDF特征提取与多头注意力，显著提升文本情感分析性能。在公开数据集上的实验表明，该方法在准确率、召回率和F1分数等关键指标上均优于基线模型。具体而言，模型在测试集上达到80.28%的准确率，相比标准LSTM提升约12%。消融实验验证了各模块必要性，其中多头注意力对性能提升贡献最大。本研究为情感分析提供了有效方案，可应用于舆情监控、产品推荐等领域。
原文摘要 · Abstract (English)
This work proposes an LSTM-based sentiment classification model with multi-head attention mechanism and TF-IDF optimization. Through the integration of TF-IDF feature extraction and multi-head attention, the model significantly improves text sentiment analysis performance. Experimental results on public data sets demonstrate that the new method achieves substantial improvements in the most critical metrics like accuracy, recall, and F1-score compared to baseline models. Specifically, the model achieves an accuracy of 80.28% on the test set, which is improved by about 12% in comparison with standard LSTM models. Ablation experiments also support the necessity and necessity of all modules, in which the impact of multi-head attention is greatest to performance improvement. This research provides a proper approach to sentiment analysis, which can be utilized in public opinion monitoring, product recommendation, etc.
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