Cone: Unsupervised Contrastive Opinion Extraction
This work addresses the challenge of extracting structured positive and negative viewpoints from text for applications like online product recommendations, representing an incremental improvement over existing unsupervised methods.
The paper tackled the problem of unsupervised contrastive opinion extraction, which often suffers from incoherent sentences and conflicting viewpoints, by proposing Cone, a model that learns disentangled latent aspect and sentiment representations; it outperformed competitive baselines on hotel review and COVID vaccine Twitter datasets without using label supervision or seed words.
Contrastive opinion extraction aims to extract a structured summary or key points organised as positive and negative viewpoints towards a common aspect or topic. Most recent works for unsupervised key point extraction is largely built on sentence clustering or opinion summarisation based on the popularity of opinions expressed in text. However, these methods tend to generate aspect clusters with incoherent sentences, conflicting viewpoints, redundant aspects. To address these problems, we propose a novel unsupervised Contrastive OpinioN Extraction model, called Cone, which learns disentangled latent aspect and sentiment representations based on pseudo aspect and sentiment labels by combining contrastive learning with iterative aspect/sentiment clustering refinement. Apart from being able to extract contrastive opinions, it is also able to quantify the relative popularity of aspects and their associated sentiment distributions. The model has been evaluated on both a hotel review dataset and a Twitter dataset about COVID vaccines. The results show that despite using no label supervision or aspect-denoted seed words, Cone outperforms a number of competitive baselines on contrastive opinion extraction. The results of Cone can be used to offer a better recommendation of products and services online.