Bilateral-brain-like Semantic and Syntactic Cognitive Network for Aspect-level Sentiment Analysis

Aspect-level sentiment analysis (ALSA) is a fine-grained task for classifying the sentiment polarity of a specific aspect in a sentence. In spite of the progress in deep-learning algorithms within natural language processing domain, the methods in line with human cognition are absent. Inspired by the processing principle of our brain, we propose a Bilateral-brainlike Semantic and Syntactic Cognitive Network (BSSCN) for aspect-level sentiment analysis. There are four major modules established in BSSCN, which are left hemisphere semantic activation (LH-SA), semantic selection (SS) module, semantic integration (SI) module and right hemisphere semantic activation (RH-SA). Experimental results on a variety of datasets show that our model stably outperforms the widely-used methods, which establishes a strong evidence of the effectiveness in ALSA tasks.