A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews
This work addresses the challenge of better interpretability in topic modeling for movie and book reviews, though it is incremental as it builds on existing neural topic models.
The authors tackled the problem of disentangling opinion topics from plot/neutral ones in user reviews by proposing a neural topic model with adversarial training, resulting in improved topic coherence, variety, and sentiment classification performance on the new MOBO dataset.
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.