  Cross-domain sentiment classification has been a hot spot these years, which
aims to learn a reliable classifier using labeled data from a source domain and
evaluate it on a target domain. In this vein, most approaches utilized domain
adaptation that maps data from different domains into a common feature space.
To further improve the model performance, several methods targeted to mine
domain-specific information were proposed. However, most of them only utilized
a limited part of domain-specific information. In this study, we first develop
a method of extracting domain-specific words based on the topic information
derived from topic models. Then, we propose a Topic Driven Adaptive Network
(TDAN) for cross-domain sentiment classification. The network consists of two
sub-networks: a semantics attention network and a domain-specific word
attention network, the structures of which are based on transformers. These
sub-networks take different forms of input and their outputs are fused as the
feature vector. Experiments validate the effectiveness of our TDAN on sentiment
classification across domains. Case studies also indicate that topic models
have the potential to add value to cross-domain sentiment classification by
discovering interpretable and low-dimensional subspaces.
