Aspect-sentiment classification in opinion mining using the combination of rule-based and machine learning
Zulva Fachrina, Dwi Hendratmo Widyantoro · 2017
Most online marketplaces in Indonesia provide review or feedback feature in order to enhance customer's satisfaction. However, there is a large number of unstructured opinions and every opinion can discuss one or more aspects. In this paper, we propose a combination of rule-based and machine learning approach to classify aspect and its sentiment of online marketplace opinions. We use Support Vector Machine and Naïve Bayes Classifier for classifying opinions. The evaluation uses 2960 reviews from various categories collected from Indonesian online marketplace site. The best method for quality, accuracy, service, communication, and delivery aspect is machine learning SVM with rule-based as one of the features while the best method for packaging and price aspect is using rule-based only. The average f-measures for all aspects ranging from 78.9% to 92%.