Tree-based data filtering for online user-generated reviews

Abstract

Analysis of online user-generated reviews has attracted extensive attention with broad applications in recent years. However, the high-volume and low-value density of online reviews bring challenges to a timely and effective data utilization. To address that challenge, this work proposes an unsupervised review filtering method based on the inherent tree-structured hierarchies among review data that reflect the general-to-specific characteristics of various quality aspects discussed in reviews. In particular, the reviews with aspects distributed near the leaf nodes of the tree are capable of providing more specific and detailed information about the examined product, which is more likely to be reserved after the tree-based filtering. To enable an effective extraction of aspect hierarchies from a broad variety of review corpora, a Bayesian nonparametric hierarchical topic model has been constructed and incorporated with an enhanced Pólya urn scheme. The approximate inference of model parameters is obtained by an efficient collapsed Gibbs sampling procedure. The proposed method can enhance the layered effect of individual reviews according to their general-to-specific characteristics and reserve an information-rich subset filtered from the raw review corpus. The merits of the proposed method have been elaborated by case studies on two real-world data sets and an extensive simulation study.