Multi-Domain Aspect Extraction Using Support Vector Machines

Nadheesh Jihan, Yasas Senarath, Dulanjaya Tennekoon, Mithila Wickramarathne, and Surangika Ranathunga Department of Computer Science and Engineering, University of Moratuwa, Katubedda 10400, Sri Lanka {nadheeshj.13, wayasas.13, dulanjayatennekoon.13, mithwick.13, surangika}@cse.mrt.ac.lk Abstract This paper describes a system to extract aspect categories for the task of aspect based sentiment analysis. This system can extract both implicit and explicit aspects. We propose a one-vs-rest Support Vector Machine (SVM) classifier preceded by a state of the art preprocessing pipeline. We present the use of mean embeddings as a feature along with two other new features to significantly improve the accuracy of the SVM classifier. This solution is extensible to customer reviews in different domains. Our results outperform the best recorded F1 score in the SemEval-2016 Task 5 dataset consisting of customer reviews from restaurant and laptop domains.