AAC-HABSA: An Adaptive Aspect Conditioning Framework for Interpretable and Robust Aspect-Based Sentiment Analysis

Abstract

: Aspect-Based Sentiment Analysis (ABSA) is a fundamental Natural Language Processing (NLP) task that aims to determine fine-grained sentiment polarity toward specific aspects mentioned in text. With the emergence of Large Language Models (LLMs) and transformer-based architectures, significant improvements have been achieved in contextual representation learning for sentiment analysis. However, existing LLM-inspired and transformer-based ABSA frameworks often suffer from inadequate aspect-context alignment, redundant feature integration, limited interpretability, and insufficient coordination between contextual and sequential modeling components. To address these challenges, this paper proposes two hybrid architectures, namely HABSA and AAC-HABSA, centered on a novel Adaptive Aspect Conditioning Layer (AACL) that injects aspect information prior to transformer-based contextual encoding. The proposed framework follows a structured pipeline comprising tokenization, token and positional embeddings, AACL, transformer encoding, BiLSTM refinement, aspect-guided attention, fully connected projection, and focal loss optimization. By conditioning token representations before contextual encoding, the framework enables aspect-aware contextual learning that better captures sentiment-relevant semantic dependencies. Subsequent sequential refinement and attention-based reasoning further enhance sentiment polarity alignment while improving model interpretability. To evaluate the proposed approach, a robust ABSA dataset containing approximately 10,000 recent reviews annotated across five sentiment intensity levels was developed. Extensive experiments demonstrate that HABSA and AAC-HABSA consistently outperform transformer-only and conventional hybrid baselines in terms of accuracy, macro-F1 score, robustness, and attention-based interpretability. The proposed framework provides a computationally efficient, mathematically coherent, and interpretable solution for fine-grained sentiment analysis. By strengthening aspect-aware representation learning within transformer and LLM-oriented sentiment analysis pipelines, this work contributes to the development of scalable and deployable AI-driven opinion analytics systems across real-world domains.