A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews

Gabriele Pergola, Lin Gui, Yulan He
<span title="">2021</span> <i title="Association for Computational Linguistics"> Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies </i> &nbsp; <span class="release-stage">unpublished</span>
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 (NTM). 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
more &raquo; ... lot 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/2021.naacl-main.228">doi:10.18653/v1/2021.naacl-main.228</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jnk4w76uobfmbgufsaqcuk5v7y">fatcat:jnk4w76uobfmbgufsaqcuk5v7y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715152139/http://wrap.warwick.ac.uk/153652/7/WRAP-disentangled-adversarial-neural-topic-model-separating-opinions-plots-user-reviews-He-2021.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/cf/5e/cf5e6000e79915b2a6b2861bece2484f880c07eb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/2021.naacl-main.228"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>