  We study Comparative Preference Classification (CPC) which aims at predicting
whether a preference comparison exists between two entities in a given sentence
and, if so, which entity is preferred over the other. High-quality CPC models
can significantly benefit applications such as comparative question answering
and review-based recommendations. Among the existing approaches, non-deep
learning methods suffer from inferior performances. The state-of-the-art graph
neural network-based ED-GAT (Ma et al., 2020) only considers syntactic
information while ignoring the critical semantic relations and the sentiments
to the compared entities. We proposed sentiment Analysis Enhanced COmparative
Network (SAECON) which improves CPC ac-curacy with a sentiment analyzer that
learns sentiments to individual entities via domain adaptive knowledge
transfer. Experiments on the CompSent-19 (Panchenko et al., 2019) dataset
present a significant improvement on the F1 scores over the best existing CPC
approaches.
