Sentiment Analysis in the Era of Large Language Models: A Reality Check (2305.15005v1)

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 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 LLMs (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}.

Sentiment Analysis in the Era of LLMs: A Reality Check

The paper "Sentiment Analysis in the Era of LLMs: A Reality Check" offers a thorough investigation into the efficacy of LLMs in the field of sentiment analysis (SA), a pivotal area in natural language processing. The paper meticulously probes the capabilities of these LLMs across a spectrum of SA tasks, comparing them against small LLMs (SLMs) to uncover their relative strengths and limitations.

Core Evaluation and Findings

The research is extensive, examining performance across 13 unique sentiment analysis tasks utilizing 26 different datasets. This examination includes conventional tasks like sentiment classification and more sophisticated tasks like aspect-based sentiment analysis (ABSA) and the analysis of subjective texts. The paper's central findings indicate that while LLMs, such as GPT-3.5 and its variants, exhibit satisfactory results in simpler tasks, they underperform in complex tasks that require a nuanced and structured understanding of sentiment, as opposed to SLMs trained on domain-specific data.

Key Observations:

• Zero-shot Learning: LLMs were found to perform adequately in zero-shot settings for straightforward tasks like binary sentiment classification. However, they struggle with tasks demanding intricate sentiment comprehension such as ABSA.
• Few-shot Learning: In few-shot contexts, LLMs outperform SLMs, hinting at their potential in environments where annotated data is sparse. This positions LLMs as a viable option when annotation resources are constrained.
• Prompt Sensitivity: The paper underscores that LLMs are sensitive to prompt design, which necessitates consistency in task instructions to accurately assess their capabilities.

SentiEval: A Comprehensive Benchmark

The paper critiques existing evaluation methodologies for their inadequacies in truly gauging LLMs' sentiment analysis abilities, noting how these practices often lack uniformity. To address these gaps, the authors propose a novel benchmark, SentiEval. This benchmark promises a more nuanced evaluation by integrating varied task instructions, promoting a realistic assessment close to practical use cases.

Implications and Future Prospects

This investigation sheds light on both the capabilities and constraints of LLMs in sentiment analysis, with several practical and theoretical implications:

• Practical Implications: Practitioners should consider utilizing LLMs primarily in tasks with fewer data resources. Simultaneously, acknowledging their limitations in complex subjective analysis tasks can guide better model deployment strategies.
• Theoretical Implications: The work calls for more refined model architectures or training paradigms that can harness the capabilities of LLMs for complex structured tasks like ABSA.
• Future Research: The paper highlights a path for future research focusing on improving LLMs' comprehension of complex linguistic nuances and structured sentiment information. Moreover, exploring adaptive models that can reflect evolving language use in sentiment analysis is crucial.

Ultimately, "Sentiment Analysis in the Era of LLMs: A Reality Check" raises critical questions about the readiness of LLMs to act as generalized tools for sentiment analysis. While LLMs exhibit considerable promise, particularly in data-scarce environments, their limitations underscore the necessity for continued research and development in this dynamic field.