Semantic Consistency Regularization with Large Language Models for Semi-supervised Sentiment Analysis

• URL: http://arxiv.org/abs/2501.17598v1
• Date: Wed, 29 Jan 2025 12:03:11 GMT
• Title: Semantic Consistency Regularization with Large Language Models for Semi-supervised Sentiment Analysis
• Authors: Kunrong Li, Xinyu Liu, Zhen Chen,
• Abstract summary: We propose a framework for semi-supervised sentiment analysis. We introduce two prompting strategies to semantically enhance unlabeled text. Experiments show our method achieves remarkable performance over prior semi-supervised methods.
• Score: 20.503153899462323
• License:
• 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.

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