Link Prediction from Heterogeneous Opinion Mining Networks with Multi-Domain Applications

How to mine opinions from pre-trained and general-purpose models in different domain applications? Answering this question is a recent research challenge since most existing methods fail when applied in domains other than those trained. In this paper, we introduce heterogeneous opinion mining networks as a strategy to combine opinion data from different domains, as well as integrate various models trained for different applications. We discuss how such knowledge results in different nodes and links in heterogeneous networks. We also investigated the mapping of the network nodes to a unified embedding space to allow vector-space-based machine learning models. We evaluate our proposal in a link prediction task to show that nodes and relationships in one domain can predict relevant knowledge in other domains.