From Prediction to Justification: Aligning Sentiment Reasoning with Human Rationale via Reinforcement Learning
Summary: arXiv:2604.13398v1 Announce Type: cross
Abstract
While Aspect-based Sentiment Analysis (ABSA) systems have achieved high accuracy in identifying sentiment polarities, they often operate as “black boxes,” lacking the explicit reasoning capabilities characteristic of human affective cognition. Humans do not merely categorize sentiment; they construct causal explanations for their judgments. To bridge this gap, we propose ABSA-R1, a large language model framework designed to mimic this “reason-before-predict” cognitive process. By leveraging reinforcement learning (RL), ABSA-R1 learns to articulate the why behind the what, generating natural language justifications that ground its sentiment predictions.
Key Features of ABSA-R1
ABSA-R1 integrates several innovative features aimed at enhancing the interpretability and performance of sentiment analysis:
- Cognition-Aligned Reward Model: This novel model enforces consistency between the generated reasoning path and the final emotional label, encouraging the system to provide logical justifications for its sentiment predictions.
- Reinforcement Learning Mechanism: By employing RL, the model is trained to optimize its reasoning process, allowing it to improve over time and refine its justifications based on feedback.
- Metacognitive Monitoring: Inspired by human cognitive processes, this feature allows the model to assess its own uncertainty and make informed decisions about when to reject its own predictions in favor of more reliable outcomes.
- Performance-Driven Rejection Sampling: This strategy selectively targets difficult cases where the model’s internal reasoning is uncertain or inconsistent, leading to more accurate sentiment classification.
Experimental Results
To evaluate the efficacy of ABSA-R1, comprehensive experiments were conducted on four benchmark datasets. The results indicate a significant improvement in both interpretability and performance compared to traditional non-reasoning baselines:
- Enhanced Interpretability: Users are provided with clear justifications for sentiment predictions, allowing for a better understanding of the model’s reasoning process.
- Superior Performance: ABSA-R1 demonstrated improved accuracy in sentiment classification and triplet extraction tasks, showcasing its capability to outperform existing models.
- Robust Handling of Complexity: The model effectively managed complex cases that previously posed challenges for traditional sentiment analysis systems.
Conclusion
The introduction of ABSA-R1 marks a significant advancement in Aspect-based Sentiment Analysis by aligning machine reasoning with human cognitive processes. By providing not just predictions but also the rationale behind them, ABSA-R1 enhances the interpretability and reliability of sentiment analysis systems. As sentiment analysis continues to play a crucial role in various applications, the ability to understand the underlying reasoning will be invaluable for users across diverse fields.