  In this paper, we propose the Brand-Topic Model (BTM) which aims to detect
brand-associated polarity-bearing topics from product reviews. Different from
existing models for sentiment-topic extraction which assume topics are grouped
under discrete sentiment categories such as `positive', `negative' and
`neural', BTM is able to automatically infer real-valued brand-associated
sentiment scores and generate fine-grained sentiment-topics in which we can
observe continuous changes of words under a certain topic (e.g., `shaver' or
`cream') while its associated sentiment gradually varies from negative to
positive. BTM is built on the Poisson factorisation model with the
incorporation of adversarial learning. It has been evaluated on a dataset
constructed from Amazon reviews. Experimental results show that BTM outperforms
a number of competitive baselines in brand ranking, achieving a better balance
of topic coherence and uniqueness, and extracting better-separated
polarity-bearing topics.
