融合多种情感模型，提升分析准确率与鲁棒性
SentiFuse: Deep Multi-model Fusion Framework for Robust Sentiment Extraction
- 通过标准化层与多策略融合，整合异构情感模型
- 特征级融合使F1得分最高提升4%（相比最优单模型）
- 自适应融合增强对否定、复杂情绪等难点的处理能力
情感分析模型各具优势，但现有方法缺乏统一集成框架。本文提出SentiFuse，一种灵活且模型无关的深度多模型融合框架，通过标准化层与多种融合策略，支持决策级、特征级及自适应融合，实现多样模型的系统化结合。在Crowdflower、GoEmotions和Sentiment140三个大规模社交媒体数据集上实验表明，SentiFuse持续优于单个模型与简单集成。特征级融合表现最佳，相较于最优单模型和平均法，F1分数最高提升4%；自适应融合则显著增强对否定、混合情绪及复杂表达等挑战性场景的鲁棒性。结果表明，系统性利用模型互补性可提升跨数据集与文本类型的情感分析精度与可靠性。
原文摘要 · Abstract (English)
Sentiment analysis models exhibit complementary strengths, yet existing approaches lack a unified framework for effective integration. We present SentiFuse, a flexible and model-agnostic framework that integrates heterogeneous sentiment models through a standardization layer and multiple fusion strategies. Our approach supports decision-level fusion, feature-level fusion, and adaptive fusion, enabling systematic combination of diverse models. We conduct experiments on three large-scale social-media datasets: Crowdflower, GoEmotions, and Sentiment140. These experiments show that SentiFuse consistently outperforms individual models and naive ensembles. Feature-level fusion achieves the strongest overall effectiveness, yielding up to 4\% absolute improvement in F1 score over the best individual model and simple averaging, while adaptive fusion enhances robustness on challenging cases such as negation, mixed emotions, and complex sentiment expressions. These results demonstrate that systematically leveraging model complementarity yields more accurate and reliable sentiment analysis across diverse datasets and text types.
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