论文标题
带有潜在情感的多光观点分析
Multi-Aspect Sentiment Analysis with Latent Sentiment-Aspect Attribution
论文作者
论文摘要
在本文中，我们介绍了一个名为“情感归因”模块（SAAM）的新框架。 SAAM在传统的神经网络之上工作，旨在解决多种情感分类和情感回归的问题。该框架通过利用句子级嵌入功能与文档级方面评分分数的变化之间的相关性来起作用。我们在基于CNN和RNN的模型之上展示了我们的框架的几种变体。酒店评论数据集和啤酒评论数据集的实验表明，SAAM可以改善相应基本模型的情感分析表现。此外，由于我们的框架将句子级别的分数结合到文档级别的分数中，因此它能够更深入地了解数据（例如，半监督的句子方面标签）。因此，我们通过详细的分析结束了论文，该分析显示了我们的模型对其他应用程序（例如情感摘要提取）的潜力。
In this paper, we introduce a new framework called the sentiment-aspect attribution module (SAAM). SAAM works on top of traditional neural networks and is designed to address the problem of multi-aspect sentiment classification and sentiment regression. The framework works by exploiting the correlations between sentence-level embedding features and variations of document-level aspect rating scores. We demonstrate several variations of our framework on top of CNN and RNN based models. Experiments on a hotel review dataset and a beer review dataset have shown SAAM can improve sentiment analysis performance over corresponding base models. Moreover, because of the way our framework intuitively combines sentence-level scores into document-level scores, it is able to provide a deeper insight into data (e.g., semi-supervised sentence aspect labeling). Hence, we end the paper with a detailed analysis that shows the potential of our models for other applications such as sentiment snippet extraction.
