论文标题
AX-MABSA：一个基于多标签方面的情感分析的框架
AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis
论文作者
论文摘要
基于方面的情感分析是一个主要的研究领域，在社交媒体分析，业务，金融和健康中具有潜在的应用。该领域的先前工作主要基于监督方法，使用弱监督的一些技术仅限于预测每个审查句子的单个方面类别。在本文中，我们提出了一个极为弱的多标签方面类别情感分析框架，该框架不使用任何标记的数据。我们只依靠每个类别的单词作为初始指示信息。我们进一步提出了一种自动选择技术来选择这些种子类别和情感单词。我们探索无监督的语言模型培训以提高整体性能，并提出一个多标签生成器模型，以每次审查句子生成多个方面类别索引对。在四个基准数据集上进行的实验展示了我们的方法，以优于其他弱监督的基线的方法。
Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. Prior works in this area are primarily based on supervised methods, with a few techniques using weak supervision limited to predicting a single aspect category per review sentence. In this paper, we present an extremely weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. We only rely on a single word per class as an initial indicative information. We further propose an automatic word selection technique to choose these seed categories and sentiment words. We explore unsupervised language model post-training to improve the overall performance, and propose a multi-label generator model to generate multiple aspect category-sentiment pairs per review sentence. Experiments conducted on four benchmark datasets showcase our method to outperform other weakly supervised baselines by a significant margin.
