Abstract
In this paper, we propose a variational approach to weakly supervised document-level multi-aspect sentiment classification. Instead of using user-generated ratings or annotations provided by domain experts, we use target-opinion word pairs as "supervision." These word pairs can be extracted by using dependency parsers and simple rules. Our objective is to predict an opinion word given a target word while our ultimate goal is to learn a sentiment polarity classifier to predict the sentiment polarity of each aspect given a document. By introducing a latent variable, i.e., the sentiment polarity, to the objective function, we can inject the sentiment polarity classifier to the objective via the variational lower bound. We can learn a sentiment polarity classifier by optimizing the lower bound. We show that our method can outperform weakly supervised baselines on TripAdvisor and BeerAdvocate datasets and can be comparable to the state-of-the-art supervised method with hundreds of labels per aspect.
Abstract (translated)
本文提出了弱监督文档级多方面情感分类的变分方法。我们不使用由领域专家提供的用户生成的评级或注释，而是使用目标意见词对作为“监督”。这些词对可以使用依赖性解析器和简单规则来提取。我们的目标是预测一个给定目标词的意见词，而我们的最终目标是学习一个情绪极性分类器来预测给定文档中每个方面的情绪极性。通过在目标函数中引入一个潜在变量，即情绪极性，我们可以通过变量下界将情绪极性分类器注入到目标函数中。我们可以通过优化下界来学习情绪极性分类器。我们表明，我们的方法可以优于TripAdvisor和BeerAdvocate数据集上的弱监督基线，并且可以与每个方面具有数百个标签的最新监督方法相比较。
URL
https://arxiv.org/abs/1904.05055
