Combination of Recursive and Recurrent Neural Networks for Aspect-Based Sentiment Analysis Using Inter-Aspect Relations

Abstract

Sentiment analysis studies in the literature mostly use either recurrent or recursive neural network models. Recurrent models capture the effect of time and propagate the information of sentiment labels in a review throughout the word sequence. Recursive models, on the other hand, extract syntactic structures from the texts and leverage the sentiment information during training. There are only a few studies that incorporate both of these models into a single neural network for the sentiment classification task. In this paper, we propose a novel neural network framework that combines recurrent and recursive neural models for aspect-based sentiment analysis. By using constituency and dependency parsers, we first divide each review into subreviews that include the sentiment information relevant to the corresponding aspect terms. After generating and training the recursive neural trees built from the parses of the subreviews, we feed their output into the recurrent model. We evaluated our ensemble approach on two datasets in English of different genres. We achieved state-of-the-art results and outperformed the baseline study by a significant margin for both domains.

Keywords

• Aspect-based sentiment classification
• ensemble neural network model
• recurrent neural networks
• recursive neural networks
• sentiment analysis