Sentiment Analysis in the Era of Large Language Models: A Reality Check

Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Pan, Lidong Bing


Abstract
Sentiment analysis (SA) has been a long-standing research area in natural language processing. With the recent advent of large language models (LLMs), there is great potential for their employment on SA problems. However, the extent to which current LLMs can be leveraged for different sentiment analysis tasks remains unclear. This paper aims to provide a comprehensive investigation into the capabilities of LLMs in performing various sentiment analysis tasks, from conventional sentiment classification to aspect-based sentiment analysis and multifaceted analysis of subjective texts. We evaluate performance across 13 tasks on 26 datasets and compare the results against small language models (SLMs) trained on domain-specific datasets. Our study reveals that while LLMs demonstrate satisfactory performance in simpler tasks, they lag behind in more complex tasks requiring a deeper understanding of specific sentiment phenomena or structured sentiment information. However, LLMs significantly outperform SLMs in few-shot learning settings, suggesting their potential when annotation resources are limited. We also highlight the limitations of current evaluation practices in assessing LLMs’ SA abilities and propose a novel benchmark, SentiEval, for a more comprehensive and realistic evaluation. Data and code are available at https://github.com/DAMO-NLP-SG/LLM-Sentiment.
Anthology ID:
2024.findings-naacl.246
Volume:
Findings of the Association for Computational Linguistics: NAACL 2024
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3881–3906
Language:
URL:
https://aclanthology.org/2024.findings-naacl.246
DOI:
Bibkey:
Cite (ACL):
Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Pan, and Lidong Bing. 2024. Sentiment Analysis in the Era of Large Language Models: A Reality Check. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3881–3906, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Sentiment Analysis in the Era of Large Language Models: A Reality Check (Zhang et al., Findings 2024)
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https://aclanthology.org/2024.findings-naacl.246.pdf
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 2024.findings-naacl.246.copyright.pdf