Sentiment analysis is a crucial task in natural language processing that
involves identifying and extracting subjective sentiment from text.
Self-training has recently emerged as an economical and efficient technique for
developing sentiment analysis models by leveraging a small amount of labeled
data and a larger amount of unlabeled data. However, the performance of a
self-training procedure heavily relies on the choice of the instance selection
strategy, which has not been studied thoroughly. This paper presents an
empirical study on various instance selection strategies for self-training on
two public sentiment datasets, and investigates the influence of the strategy
and hyper-parameters on the performance of self-training in various few-shot
settings.

情感分析是自然语言处理中的一个关键任务，通过从文本中识别和提取主观情感。自我训练是一种最近出现的经济高效的技术，通过利用少量标记数据和大量无标记数据来开发情感分析模型。然而，自我训练过程的性能在很大程度上取决于实例选择策略的选择，这一策略尚未进行全面研究。本文在两个公开情感数据集上对自我训练的各种实例选择策略进行了经验性研究，并探讨了策略和超参数在各种小样本设置中自我训练性能的影响。