用图模型自动标注文本，大幅减少情感分析所需人工标签。
Reducing Labeling Costs in Sentiment Analysis via Semi-Supervised Learning
- 基于最近邻图和余弦相似度传播标签，生成未标注数据的伪标签。
- 在情感分析任务中，仅需少量真实标签即可达到接近全监督的效果。
- 适合标注成本高、数据量大的场景，如社交媒体情绪监测。
标注数据集是机器学习中的重大挑战，涉及成本与时间开销。本研究提出一种基于流形假设的归纳式标签传播方法，用于文本分类。通过构建基于网络嵌入的最近邻图，利用余弦相似度扩展标签，生成未标注数据的伪标签，并将其融入深度神经网络训练。该方法将无监督数据纳入有监督学习流程，显著降低对人工标注的依赖。基于其他领域已有的成功经验，本研究验证了该方法在情感分析任务中的有效性，为半监督学习提供了实用方案。
原文摘要 · Abstract (English)
Labeling datasets is a noteworthy challenge in machine learning, both in terms of cost and time. This research, however, leverages an efficient answer. By exploring label propagation in semi-supervised learning, we can significantly reduce the number of labels required compared to traditional methods. We employ a transductive label propagation method based on the manifold assumption for text classification. Our approach utilizes a graph-based method to generate pseudo-labels for unlabeled data for the text classification task, which are then used to train deep neural networks. By extending labels based on cosine proximity within a nearest neighbor graph from network embeddings, we combine unlabeled data into supervised learning, thereby reducing labeling costs. Based on previous successes in other domains, this study builds and evaluates this approach's effectiveness in sentiment analysis, presenting insights into semi-supervised learning.
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