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

Cem Rifki Aydin,Tunga Gungor

doi:10.1109/access.2020.2990306

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.

Highlights

Sentiment analysis is the task of identification and quantification of sentiments in reviews
The third one is the recurrent neural network model, which is used as the baseline by itself in this study
The proposed ensemble approach is formed of a recurrent model which is used as the baseline and a recursive model

Summary

INTRODUCTION

Sentiment analysis is the task of identification and quantification of sentiments in reviews. Recurrent models like the above make use of the sequence information in a series of objects This helps propagate the impact of the sentiments to the preceding or succeeding words in a text. The use of recursive neural networks can, be useful to assign the same or similar sentiments to the words located in the same subtrees of the parsed text Incorporating this structural and sentiment information captured by recursive neural networks into other neural network structures, such as recurrent models, can yield a more comprehensive and robust framework. As for the recursive module, we develop novel ways for extracting subreviews, which correspond to aspect term groups, from reviews.

RELATED WORK

SUBMODEL 2

SUBMODEL 1

EXPERIMENTAL EVALUATION

Findings

CONCLUSION AND FUTURE WORK