负置信度感知的弱监督二元分类算法在有效的评论有用性分类中的应用
Negative Confidence-Aware Weakly Supervised Binary Classification for Effective Review Helpfulness Classification
摘要：
正标签的不完整性和存在许多未标记的实例是二元分类应用中常见的问题，例如在评论有用性分类中。分类文献中的各种研究将所有未标记的实例视为反例。然而，一个分类模型，学习分类二进制实例与不完整的积极标签，同时假设所有未标记的数据是负面的例子，往往会产生一个有偏见的分类器。在这项工作中，我们提出了一种新的负置信度感知的弱监督方法（NCWS），该方法通过在分类器的训练过程中区分具有不同负置信度的未标记样本来定制二进制分类损失函数。我们使用的审查有益分类作为测试案例，检查我们的NCWS方法的有效性。我们使用三个不同的数据集，即一个来自Yelp（场地评论），两个来自亚马逊（Kindle和电子评论），对NCWS进行了全面评估。我们的研究结果表明，NCWS优于强大的基线从文献中，包括现有的基于SVM的方法（即SVM-P），积极和未标记的基于学习的方法（即C-PU）和积极的信心为基础的方法（即P-conf）在解决分类器的偏见问题。此外，我们还通过在最先进的基于评论的场馆推荐模型（即DeepCoNN）中使用其分类的有用评论来进一步检查NCWS的有效性，并展示了使用NCWS在增强场馆推荐有效性方面的好处与基线相比。
英文摘要：
The incompleteness of positive labels and the presence of many unlabelled instances are common problems in binary classification applications such as in review helpfulness classification. Various studies from the classification literature consider all unlabelled instances as negative examples. However, a classification model that learns to classify binary instances with incomplete positive labels while assuming all unlabelled data to be negative examples will often generate a biased classifier. In this work, we propose a novel Negative Confidence-aware Weakly Supervised approach (NCWS), which customises a binary classification loss function by discriminating the unlabelled examples with different negative confidences during the classifier's training. We use the review helpfulness classification as a test case for examining the effectiveness of our NCWS approach. We thoroughly evaluate NCWS by using three different datasets, namely one from Yelp (venue reviews), and two from Amazon (Kindle and Electronics reviews). Our results show that NCWS outperforms strong baselines from the literature including an existing SVM-based approach (i.e. SVM-P), the positive and unlabelled learning-based approach (i.e. C-PU) and the positive confidence-based approach (i.e. P-conf) in addressing the classifier's bias problem. Moreover, we further examine the effectiveness of NCWS by using its classified helpful reviews in a state-of-the-art review-based venue recommendation model (i.e. DeepCoNN) and demonstrate the benefits of using NCWS in enhancing venue recommendation effectiveness in comparison to the baselines.
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