Self-training Strategies for Sentiment Analysis: An Empirical Study

TL;DR

This paper empirically evaluates different self-training strategies for sentiment analysis, examining the impact of strategies and hyper-parameters on small models and exploring the use of large language models to enhance self-training.

Contribution

It provides a comprehensive empirical comparison of self-training strategies for sentiment analysis, including the use of large language models for improved performance.

Findings

Self-training strategies significantly affect model performance.

Hyper-parameters play a crucial role in self-training effectiveness.

Leveraging large language models can enhance self-training outcomes.

Abstract

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 large amount of unlabeled data. However, given a set of training data, how to utilize them to conduct self-training makes a significant difference in the final performance of the model. We refer to this methodology as the self-training strategy. In this paper, we present an empirical study of various self-training strategies for sentiment analysis. First, we investigate the influence of the self-training strategy and hyper-parameters on the performance of traditional small language models (SLMs) in various few-shot settings. Second, we also explore the feasibility of leveraging large language models (LLMs) to help self-training. We propose and empirically compare several self-training strategies with the intervention of LLMs. Extensive experiments are conducted on three real-world sentiment analysis datasets.