An Empirical Study on Instance Selection Strategies in Self-training for Sentiment Analysis

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.