  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.
