Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation Learning

TL;DR

This paper introduces THGRL, a novel graph-based method for cross-domain aspect category detection that leverages user behavior data to handle feature space, distribution, and output space differences, outperforming existing models.

Contribution

The paper proposes a new traceable heterogeneous graph learning approach with a latent Walker Tracer to improve cross-domain aspect detection across diverse data settings.

Findings

Outperforms state-of-the-art baseline models

Effectively handles feature space and distribution differences

Utilizes user behavior data for improved detection

Abstract

Aspect category detection is an essential task for sentiment analysis and opinion mining. However, the cost of categorical data labeling, e.g., label the review aspect information for a large number of product domains, can be inevitable but unaffordable. In this study, we propose a novel problem, cross-domain aspect category transfer and detection, which faces three challenges: various feature spaces, different data distributions, and diverse output spaces. To address these problems, we propose an innovative solution, Traceable Heterogeneous Graph Representation Learning (THGRL). Unlike prior text-based aspect detection works, THGRL explores latent domain aspect category connections via massive user behavior information on a heterogeneous graph. Moreover, an innovative latent variable "Walker Tracer" is introduced to characterize the global semantic/aspect dependencies and capture the informative vertexes on the random walk paths. By using THGRL, we project different domains' feature spaces into a common one, while allowing data distributions and output spaces stay differently. Experiment results show that the proposed method outperforms a series of state-of-the-art baseline models.