Modeling inter-aspect dependencies for aspect-based sentiment analysis

Abstract

Aspect-based Sentiment Analysis is a finegrained task of sentiment classification for multiple aspects in a sentence. Present neuralbased models exploit aspect and its contextual information in the sentence but largely ignore the inter-aspect dependencies. In this paper, we incorporate this pattern by simultaneous classification of all aspects in a sentence along with temporal dependency processing of their corresponding sentence representations using recurrent networks. Results on the benchmark SemEval 2014 dataset suggest the effectiveness of our proposed approach.