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        <title>Self-training Strategies for Sentiment Analysis: An Empirical Study</title>
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        <namePart type="given">Haochen</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Self-training Strategies for Sentiment Analysis: An Empirical Study
%A Liu, Haochen
%A Rallabandi, Sai
%A Wu, Yijing
%A Dakle, Parag
%A Raghavan, Preethi
%Y Graham, Yvette
%Y Purver, Matthew
%S Findings of the Association for Computational Linguistics: EACL 2024
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F liu-etal-2024-self
%X 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.
%U https://aclanthology.org/2024.findings-eacl.131
%P 1944-1954

Markdown (Informal)

[Self-training Strategies for Sentiment Analysis: An Empirical Study](https://aclanthology.org/2024.findings-eacl.131) (Liu et al., Findings 2024)

• Self-training Strategies for Sentiment Analysis: An Empirical Study (Liu et al., Findings 2024)

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.