Improvement of user review classification using keyword expansion

Abstract

Application users can submit reviews for downloaded applications. Recently, developers have received more and more user reviews. However, it is still difficult to extract beneficial comments from a large amount of reviews. Latent Dirichlet Allocation (LDA) is a promising way of topic modeling, which classifies documents according to implicit multiple topics. However, there is a gap between the documents that the developer wants to extract and the document extracted by LDA. In this paper, we propose a method to extract documents of each category, such as requirements descriptions or bug reports, more accurately. Our method first decomposes the topics. Then, the method uses the keyword list which is a set of semantically similar words collected by word2vec, to integrate the decomposed topics. We apply our method to the applications user reviews in Apple Store and demonstrate the validity of it. Our approach can help application developers to extract beneficial information.