  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.

Computation and Language