Aspect-based sentiment analysis based on dynamic attention GRU

Abstract

Aspect-level sentiment analysis is a fine-grained task that aims to identify the sentiment polarity (i.e., negative, neutral, or positive) of a specific target opinion in its context. Since the sentiment polarity of a target depends on the target itself and the semantics of the context, the target and the sentence should be treated equally and modeled interactively. For aspect-level sentiment analysis, we propose (1) a method to encode the aspect and sentence simultaneously, and (2) a neural network based on a dynamic attention gated recurrent unit. The simultaneous encoding manner can generate the target representation, which contains more contextual clues. The dynamic attention mechanism can achieve the attention values of contextual words and further generate the target representation dynamically. Experimental results achieved on a SemEval 2014 dataset (Laptop and Restaurant) show that our approach achieves a significant improvement in the accuracy rates over the standard attention-based models.