SentiFuse: Deep Multi-model Fusion Framework for Robust Sentiment Extraction
This work addresses the challenge of effectively combining diverse sentiment analysis models for researchers and practitioners, though it appears incremental as it builds on existing fusion concepts.
The authors tackled the problem of integrating complementary sentiment analysis models by developing SentiFuse, a flexible framework that supports multiple fusion strategies. Their experiments on three social-media datasets showed that feature-level fusion achieved up to 4% absolute improvement in F1 score over individual models and naive ensembles.
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.