Aspect Sentiment Quad Prediction (ASQP) is a fundamental yet challenging task in fine-grained sentiment analysis, particularly when aspects or opinions are implicit. Existing methods often lack explainability and generalization, making it difficult to justify inference decisions and to detect implicit sentiment across domains and varied expression patterns. To address these limitations, we propose Tree-CoT-RT, an explainable multi-path tree-guided chain-of-thought and reinforcement learning framework specifically designed for ASQP. The core idea is to use sentiment tree structures to design type-specific reasoning templates that guide LLMs in generating explainable chains, including both final sentiment quadruples and intermediate inference steps for transparent implicit reasoning. However, the generated reasoning chains often vary in quality and may contain logical inconsistencies. To mitigate this, we introduce a reinforcement learning strategy with a rule-based reward function to generate high-quality reasoning traces, which are then used to fine-tune the LLM and enable controlled sampling. Experiments on benchmark datasets demonstrate that Tree-CoT-RT substantially outperforms strong baselines, particularly in scenarios involving implicit sentiment analysis.

Aspect sentiment quad prediction (ASQP) is crucial in aspect-based sentiment analysis (ABSA). It involves identifying a text’s aspect,sentiment, opinion, and category. Existing methods have insufficiently explored how to effectively leverage the knowledge of pre-trainedlanguage models (PLMs) to handle implicit aspects and opinions, particularly in combinations such as implicit aspect & explicit opinion, explicit aspect & implicit opinion, and implicit aspect & implicit opinion. We introduce ITSCL, a framework leveraging Instruction Tuning and Supervised Contrastive Learning to improve aspect sentiment quad predictions, especially for implicit aspects and opinions. Implementing this approach presents several challenges. First, designing effective instructions and prompts to optimize the model’s training is difficult. Second, creating sentiment combination vectors with contrastive learning to enhance the model’s discrimination requires further investigation. To address these challenges, ITSCL combines instruction tuning with aligned PLM templates, enabling better knowledge acquisition and identification of implicit sentiments. Additionally, the contrastive learning framework enhances performance by using four fully connected layers to combine sentiments, aspects, opinions, and combinations, maximizing similarity for same-label representationsand minimizing it for different labels. Experimental results show our method significantly outperforms previous methods on benchmark datasets.