Sentiment Quantification aims to detect the overall sentiment polarity of users from a set of reviews corresponding to a target. Existing methods equally treat and aggregate individual reviews’ sentiment to judge the overall sentiment polarity. However, the confidence of each review is not equal in sentiment quantification where sentiment perturbation arising from high- and low-confidence reviews may degrade the accuracy of Sentiment Quantification. Specifically, fake reviews with deceptive sentiments are low confidence, which perturbs the overall sentiment prediction. Whereas, some reviews generated by responsible users are high confidence. They contain authoritative suggestions so they should be emphasized in Sentiment Quantification. In this paper, we design and build COSE, a confidence-aware sentiment quantification framework, which can measure the confidence of individual reviews to eliminate sentiment perturbation and facilitate sentiment quantification. We design a Review Graph that achieves review confidence modeling in an unsupervised manner and obtains review confidence representations. Moreover, we develop a dynamic fusion attention mechanism, which produces sentiment “de-perturbation” vectors to eliminate the sentiment perturbation based on the confidence representations. Extensive experiments on large-scale review datasets validate the significant superiority of COSE over the state-of-the-art.

中文翻译：

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通过情绪扰动建模进行信心感知情绪量化


情感量化旨在从与目标相对应的一组评论中检测用户的整体情感极性。现有方法平等地对待和聚合个体评论的情绪来判断整体情绪极性。然而，在情感量化中，每个评论的置信度并不相等，高置信度评论和低置信度评论引起的情感扰动可能会降低情感量化的准确性。具体来说，具有欺骗性情绪的虚假评论的置信度较低，这会扰乱整体情绪预测。然而，负责任的用户生成的一些评论可信度很高。它们包含权威建议，因此在情感量化中应予以强调。在本文中，我们设计并构建了 COSE，一个信心感知的情绪量化框架，它可以测量个人评论的置信度，以消除情绪扰动并促进情绪量化。我们设计了一个评论图，以无监督的方式实现评论置信度建模并获得评论置信度表示。此外，我们开发了一种动态融合注意机制，它产生情感“去扰动”向量，以消除基于置信度表示的情感扰动。对大规模评论数据集的大量实验验证了 COSE 相对于最先进技术的显着优势。