  Monitoring online customer reviews is important for business organisations to
measure customer satisfaction and better manage their reputations. In this
paper, we propose a novel dynamic Brand-Topic Model (dBTM) which is able to
automatically detect and track brand-associated sentiment scores and
polarity-bearing topics from product reviews organised in temporally-ordered
time intervals. dBTM models the evolution of the latent brand polarity scores
and the topic-word distributions over time by Gaussian state space models. It
also incorporates a meta learning strategy to control the update of the
topic-word distribution in each time interval in order to ensure smooth topic
transitions and better brand score predictions. It has been evaluated on a
dataset constructed from MakeupAlley reviews and a hotel review dataset.
Experimental results show that dBTM outperforms a number of competitive
baselines in brand ranking, achieving a good balance of topic coherence and
uniqueness, and extracting well-separated polarity-bearing topics across time
intervals.
