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

TL;DR

This paper evaluates large language models' capabilities in sentiment analysis across various tasks and datasets, revealing strengths in simple tasks and few-shot learning, but limitations in complex sentiment understanding.

Contribution

It provides a comprehensive assessment of LLMs for sentiment analysis, introduces the SentiEval benchmark, and compares LLMs with domain-specific small models.

Findings

LLMs perform well on simple sentiment tasks

LLMs outperform small models in few-shot learning

Current evaluation practices are insufficient for complex sentiment tasks

Abstract

Sentiment analysis (SA) has been a long-standing research area in natural language processing. It can offer rich insights into human sentiments and opinions and has thus seen considerable interest from both academia and industry. With the advent of large language models (LLMs) such as ChatGPT, there is a great potential for their employment on SA problems. However, the extent to which existing 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 deeper understanding 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, \textsc{SentiEval}, for a more comprehensive and realistic evaluation. Data and code during our investigations are available at \url{https://github.com/DAMO-NLP-SG/LLM-Sentiment}.