Product aspect identification: Analyzing role of different classifiers

With the rapid advancement of eCommerce, it has become a common trend for customers to write reviews about any product they purchase. For certain popular products, such as cell phones, laptops, tablets, the number of reviews can be hundreds or even thousands, making it difficult for potential customers to identify specific aspect based overview of the product (for example, screen, camera, battery etc). This paper studies different classifiers for aspect identification from unlabeled free-form textual customer reviews. Firstly, a multi-aspect classification is proposed to learn implicit and explicit aspect-related context from the reviews for aspect identification, which does not require any manually labeled training data. Secondly, extensive experiments for analyzing the effectiveness of classifiers and feature selection for aspect identification have also been shown. The results of our experiments on smartphone reviews from Amazon show that Support Vector Machine's accuracy in aspect identification is best, followed by Random Forest and Naive Bayes.