# Can Large Language Models be Effective Online Opinion Miners?

arXiv: https://arxiv.org/abs/2505.15695 · HTML: https://mlsanity.com/paper/2505.15695/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 49 references that are in the map.
- First posted on arXiv 21 May 2025.

## Builds on (most cited first, 18 of 18)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Large Language Models for Data Annotation and Synthesis: A Survey](https://mlsanity.com/paper/2402.13446/) (2024)
- [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1911.01616/) (2019)
- [Aspect Sentiment Quad Prediction as Paraphrase Generation](https://mlsanity.com/paper/2110.00796/) (2021)
- [LMDX: Language Model-based Document Information Extraction and Localization](https://mlsanity.com/paper/2309.10952/) (2023)
- [Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Bias](https://mlsanity.com/paper/2401.01989/) (2024)
- [DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis](https://mlsanity.com/paper/2211.05705/) (2022)
- [Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation](https://mlsanity.com/paper/2303.03608/) (2023)
- [One Prompt To Rule Them All: LLMs for Opinion Summary Evaluation](https://mlsanity.com/paper/2402.11683/) (2024)
- [A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction](https://mlsanity.com/paper/2402.13364/) (2024)
- [Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy](https://mlsanity.com/paper/2403.00354/) (2024)
- [Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting](https://mlsanity.com/paper/2410.01154/) (2024)
- [Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations](https://mlsanity.com/paper/2204.10714/) (2022)
- [Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments](https://mlsanity.com/paper/2410.03594/) (2024)
- [MT-RAIG: Novel Benchmark and Evaluation Framework for Retrieval-Augmented Insight Generation over Multiple Tables](https://mlsanity.com/paper/2502.11735/) (2025)
- [Prompted Aspect Key Point Analysis for Quantitative Review Summarization](https://mlsanity.com/paper/2407.14049/) (2024)

## Built on by (most cited first, 3 of 3)

- [AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shopping](https://mlsanity.com/paper/2602.12315/) (2026)
- [IPQA: A Benchmark for Core Intent Identification in Personalized Question Answering](https://mlsanity.com/paper/2510.23536/) (2025)
- [Offline Reasoning for Efficient Recommendation: LLM-Empowered Persona-Profiled Item Indexing](https://mlsanity.com/paper/2602.21756/) (2026)

## Abstract (arXiv)

The surge of user-generated online content presents a wealth of insights into customer preferences and market trends. However, the highly diverse, complex, and context-rich nature of such contents poses significant challenges to traditional opinion mining approaches. To address this, we introduce Online Opinion Mining Benchmark (OOMB), a novel dataset and evaluation protocol designed to assess the ability of large language models (LLMs) to mine opinions effectively from diverse and intricate online environments. OOMB provides extensive (entity, feature, opinion) tuple annotations and a comprehensive opinion-centric summary that highlights key opinion topics within each content, thereby enabling the evaluation of both the extractive and abstractive capabilities of models. Through our proposed benchmark, we conduct a comprehensive analysis of which aspects remain challenging and where LLMs exhibit adaptability, to explore whether they can effectively serve as opinion miners in realistic online scenarios. This study lays the foundation for LLM-based opinion mining and discusses directions for future research in this field.