Cone: Unsupervised Contrastive Opinion Extraction
- URL: http://arxiv.org/abs/2305.04599v1
- Date: Mon, 8 May 2023 10:18:30 GMT
- Title: Cone: Unsupervised Contrastive Opinion Extraction
- Authors: Runcong Zhao, Lin Gui, Yulan He
- Abstract summary: We propose a novel unsupervised Contrastive OpinioN Extraction model, called Cone.
It learns disentangled latent aspect and sentiment representations based on pseudo aspect and sentiment labels.
It is also able to quantify the relative popularity of aspects and their associated sentiment distributions.
- Score: 29.794986769537743
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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