Self-training Strategies for Sentiment Analysis: An Empirical Study
Haochen Liu, Sai Rallabandi, Yijing Wu, Parag Dakle, Preethi Raghavan
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.- Anthology ID:
- 2024.findings-eacl.131
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2024
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Yvette Graham, Matthew Purver
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1944–1954
- Language:
- URL:
- https://aclanthology.org/2024.findings-eacl.131
- DOI:
- Cite (ACL):
- Haochen Liu, Sai Rallabandi, Yijing Wu, Parag Dakle, and Preethi Raghavan. 2024. Self-training Strategies for Sentiment Analysis: An Empirical Study. In Findings of the Association for Computational Linguistics: EACL 2024, pages 1944–1954, St. Julian’s, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Self-training Strategies for Sentiment Analysis: An Empirical Study (Liu et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.findings-eacl.131.pdf