ATE-SPD: simultaneous extraction of aspect-term and aspect sentiment polarity using Bi-LSTM-CRF neural network

Abstract

ABSTRACT Aspect-based sentiment analysis is one of the challenging problems among the various type of tasks in sentiment analysis. Sequential models specifically deep neural networks (like Recurrent Neural Networks) have been found to handle this problem in an efficient way. This paper presents a deep neural network model named ATE-SPD for aspect-based sentiment analysis that simultaneously extracts aspect-terms and their corresponding polarities in review sentences. This problem can be solved as a sequence labelling problem. We are using Bi-LSTM hybridised with CRF as both of these approaches are state-of-the-art approaches for sequence labelling tasks. It was observed that CRF is able to improve the performance of the traditional Bi-LSTM model. Another important contribution of this paper is that it provides a novel set of sequential tags for extracting aspect-terms along with their sentiment polarities. Aspect-terms and their polarities are determined without explicitly labelling the sentiment terms. The ATE-SPD is evaluated using a benchmark dataset of SemEval’14-Task4 and obtains state-of-the-art performance.