Aspect-based sentiment analysis (ABSA) aims to identify and extract opinion targets and their associated sentiments from user-generated content, enabling businesses to gain valuable insights. However, capturing the intricate interplay between aspects and their contexts remains a challenge, especially for deep learning models that struggle to effectively integrate syntactic and semantic information. Drawing inspiration from the principles of neural information processing, we propose the Neural Aspect Sentiment Analysis (Neural-ASA) framework to address these challenges. Central to Neural-ASA is the neural syncretic encoder (NSE), which leverages neural synchronization and oscillatory dynamics to capture the complex interactions between words and their syntactic relationships, forming rich, context-aware representations. Furthermore, the neural attentive sentiment aggregator (NASA) emulates the brain’s attentional processes to selectively focus on sentiment-bearing aspects while ignoring irrelevant information. Experimental evaluations on the SemEval dataset demonstrate Neural-ASA’s superiority over state-of-the-art ABSA methods and large language models, achieving impressive performance while maintaining interpretability and computational efficiency. Neural-ASA offers valuable insights into the potential of brain-inspired approaches for sentiment analysis, paving the way for more neurologically-grounded NLP models.