A Hybrid Approach for Aspect Extraction from Customer Reviews

Abstract

Aspect Extraction from consumer reviews has become an essential factor for successful Aspect Based Sentiment Analysis. Typical user trends to mention his opinion against several aspects in a single review; therefore, aspect extraction has been tackled as a multi-label classification task. Due to its complexity and the variety across different domains, yet, no single system has been able to achieve comparable accuracy levels to the human-accuracy. However, novel neural network architectures and hybrid approaches have shown promising results for aspect extraction. (Support Vector Machines) SVMs and (Convolutional Neural Networks) CNNs pose a viable solution to the multi-label text classification task and has been successfully applied to identify aspects in reviews. In this paper, we first define an improved CNN architecture for aspect extraction which achieves comparable results against the current state-of-the-art systems. Then we propose a mixture of classifiers for aspect extraction, combining the proposed improved CNN with an SVM that uses the state-of-the-art manually engineered features. The combined system outperforms the results of individual systems while showing a significant improvement over the state-of-the-art aspect extraction systems that employ complex neural architectures such as MTNA.