  The flexibility of the inference process in Variational Autoencoders (VAEs)
has recently led to revising traditional probabilistic topic models giving rise
to Neural Topic Models (NTMs). Although these approaches have achieved
significant results, surprisingly very little work has been done on how to
disentangle the latent topics. Existing topic models when applied to reviews
may extract topics associated with writers' subjective opinions mixed with
those related to factual descriptions such as plot summaries in movie and book
reviews. It is thus desirable to automatically separate opinion topics from
plot/neutral ones enabling a better interpretability. In this paper, we propose
a neural topic model combined with adversarial training to disentangle opinion
topics from plot and neutral ones. We conduct an extensive experimental
assessment introducing a new collection of movie and book reviews paired with
their plots, namely MOBO dataset, showing an improved coherence and variety of
topics, a consistent disentanglement rate, and sentiment classification
performance superior to other supervised topic models.
