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.
Highlights
Customer reviews have become the means of expressing opinions and views of consumers towards different aspects of products and services
We show that the use of non-static Convolutional Neural Networks (CNN) models perform better than static models for aspect extraction, in the absence of word2vec models trained with domain-specific corpora
Using skip-gram trained word2vec, we were able to increase the accuracy of the CNN model significantly compared to the Continuous bag of word (CBOW) trained word2vec model
Summary
Customer reviews have become the means of expressing opinions and views of consumers towards different aspects of products and services. The information contained in such reviews can be leveraged by customers to identify the best available products/ services in the market and by the organizations to identify and satisfy customer needs. Automatic sentiment analysis of customer reviews has, become a priority for the research community in recent years. Conventional sentiment analysis of text focuses on the opinion of the entire text or the sentence. In the case of consumer reviews, it has been observed that customers often talk about multiple aspects of an entity and express an opinion on each aspect separately rather than expressing the opinion towards the entity. Aspect Based Sentiment Analysis (ABSA) has emerged to tackle this issue
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
