# Large language models for aspect-based sentiment analysis

arXiv: https://arxiv.org/abs/2310.18025 · HTML: https://mlsanity.com/paper/2310.18025/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 27 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)

## Built on by (most cited first, 17 of 17)

- [Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought](https://mlsanity.com/paper/2401.06836/) (2024)
- [A Vision for Auto Research with LLM Agents](https://mlsanity.com/paper/2504.18765/) (2025)
- [Reasoning Elicitation in Language Models via Counterfactual Feedback](https://mlsanity.com/paper/2410.03767/) (2024)
- [Large Language Models for Subjective Language Understanding: A Survey](https://mlsanity.com/paper/2508.07959/) (2025)
- [Does Reasoning Emerge? Examining the Probabilities of Causation in Large Language Models](https://mlsanity.com/paper/2408.08210/) (2024)
- [A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2412.02279/) (2024)
- [Evaluating Zero-Shot Multilingual Aspect-Based Sentiment Analysis with Large Language Models](https://mlsanity.com/paper/2412.12564/) (2024)
- [Balanced Training Data Augmentation for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2507.09485/) (2025)
- [Large Language Models Enhanced by Plug and Play Syntactic Knowledge for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2506.12991/) (2025)
- [LLaMA-Based Models for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2508.08649/) (2025)
- [DS2-ABSA: Dual-Stream Data Synthesis with Label Refinement for Few-Shot Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2412.14849/) (2024)
- [In-Context Learning for Long-Context Sentiment Analysis on Infrastructure Project Opinions](https://mlsanity.com/paper/2410.11265/) (2024)
- [Single-Pass, Depth-Selective Reading for Multi-Aspect Sentiment Analysis](https://mlsanity.com/paper/2605.20998/) (2026)
- [Large-Scale Aspect-Based Sentiment Analysis with Reasoning-Infused LLMs](https://mlsanity.com/paper/2601.03940/) (2026)
- [LLM-as-an-Annotator: Training Lightweight Models with LLM-Annotated Examples for Aspect Sentiment Tuple Prediction](https://mlsanity.com/paper/2603.01778/) (2026)
- [LASQ: A Low-resource Aspect-based Sentiment Quadruple Extraction Dataset](https://mlsanity.com/paper/2604.10417/) (2026)
- [Zero-Shot to Full-Resource: Cross-lingual Transfer Strategies for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2604.26619/) (2026)

## Abstract (arXiv)

Large language models (LLMs) offer unprecedented text completion capabilities. As general models, they can fulfill a wide range of roles, including those of more specialized models. We assess the performance of GPT-4 and GPT-3.5 in zero shot, few shot and fine-tuned settings on the aspect-based sentiment analysis (ABSA) task. Fine-tuned GPT-3.5 achieves a state-of-the-art F1 score of 83.8 on the joint aspect term extraction and polarity classification task of the SemEval-2014 Task 4, improving upon InstructABSA [@scaria_instructabsa_2023] by 5.7%. However, this comes at the price of 1000 times more model parameters and thus increased inference cost. We discuss the the cost-performance trade-offs of different models, and analyze the typical errors that they make. Our results also indicate that detailed prompts improve performance in zero-shot and few-shot settings but are not necessary for fine-tuned models. This evidence is relevant for practioners that are faced with the choice of prompt engineering versus fine-tuning when using LLMs for ABSA.