Aspect-Based Sentiment Analysis (ABSA): A Review of Techniques and Applications

Aspect-Based Sentiment Analysis (ABSA) is a specialized subfield of sentiment analysis that identifies opinions toward specific aspects or attributes within text. Unlike traditional sentiment analysis, which provides only overall polarity, ABSA offers fine-grained insights by linking sentiments to distinct elements such as “battery life“ or “service quality.“ This review synthesizes advances in ABSA from rule-based and machine learning models to deep learning and transformer-based architectures. Benchmark datasets such as SemEval, Amazon reviews, and Twitter corpora are analyzed alongside applications in e-cornmerce, healthcare, education, and social media monitoring. Results from recent studies highlight the superiority of transformer-based models, which achieve state-of-the-art performance across multiple benchmarks. Key challenges include implicit aspect recognition, sarcasm detection, domain adaptation, and the scarcity of multilingual datasets. The paper concludes with recommendations for future research on explain ability, cross-domain adaptability, and few-shot or zero-shot learning, providing both academics and practitioners with a comprehensive foundation for advancing ABSA.