Abstract
Accurate sentiment analysis of texts is crucial for a variety of applications, such as understanding customer feedback, monitoring market trends, and detecting public sentiment. However, manually annotating large sentiment corpora for supervised learning is labor-intensive and time-consuming. Therefore, it is essential and effective to develop a semi-supervised method for the sentiment analysis task. Although some methods have been proposed for semi-supervised text classification, they rely on the intrinsic information within the unlabeled data and the learning capability of the NLP model, which lack generalization ability to the sentiment analysis scenario and may prone to overfit. Inspired by the ability of pretrained Large Language Models (LLMs) in following instructions and generating coherent text, we propose a Semantic Consistency Regularization with Large Language Models (SCR) framework for semi-supervised sentiment analysis. We introduce two prompting strategies to semantically enhance unlabeled text using LLMs. The first is Entity-based Enhancement (SCR-EE), which involves extracting entities and numerical information, and querying the LLM to reconstruct the textual information. The second is Concept-based Enhancement (SCR-CE), which directly queries the LLM with the original sentence for semantic reconstruction. Subsequently, the LLM-augmented data is utilized for a consistency loss with confidence thresholding, which preserves high-quality agreement samples to provide additional supervision signals during training. Furthermore, to fully utilize the uncertain unlabeled data samples, we propose a class re-assembling strategy inspired by the class space shrinking theorem. Experiments show our method achieves remarkable performance over prior semi-supervised methods.
Abstract (translated)
准确的情感分析对于理解客户反馈、监控市场趋势和检测公众情感等众多应用至关重要。然而，为监督学习手动标注大量情感语料库既耗时又费力。因此，开发一种适用于情感分析任务的半监督方法是必要且有效的。虽然已经提出了一些用于半监督文本分类的方法，但这些方法依赖于未标记数据中的内在信息和NLP模型的学习能力，这在情感分析场景中缺乏泛化能力，并可能容易过拟合。受预训练大规模语言模型（LLM）遵循指令并生成连贯文本的能力的启发，我们提出了一种基于大规模语言模型的语义一致性正则化（SCR）框架用于半监督情感分析。我们介绍了两种提示策略，利用LLM来增强未标记文本的语义信息：一种是基于实体的增强（SCR-EE），涉及提取实体和数值信息，并查询LLM以重构文本信息；另一种是基于概念的增强（SCR-CE），直接使用原始句子查询LLM进行语义重构。随后，利用经过LLM扩充的数据应用一致性损失并采用置信度阈值筛选高质量的一致性样本，在训练过程中提供额外的监督信号。此外，为了充分利用不确定的未标记数据样本，我们提出了一种基于类别重组策略的方法，该方法受到类空间收缩定理的启发。实验结果表明，我们的方法在以前的半监督方法中取得了显著的效果。
URL
https://arxiv.org/abs/2501.17598
