mlsanityData to 14 Oct 2026

Can Large Language Models be Effective Online Opinion Miners?

arXiv 2505.15695 ↗First posted 21 May 2025cs.CLcited by 3

Not on the map. Why?

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.

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

18 of 49 references.

References · 18

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  3. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  4. 2024Large Language Models for Data Annotation and Synthesis: A Surveycited by 205
  5. 2019Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysiscited by 123
  6. 2021Aspect Sentiment Quad Prediction as Paraphrase Generationcited by 100
  7. 2023LMDX: Language Model-based Document Information Extraction and Localizationcited by 37
  8. 2024Revisiting Zero-Shot Abstractive Summarization in the Era of Large Language Models from the Perspective of Position Biascited by 28
  9. 2022DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysiscited by 27
  10. 2023Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluationcited by 24
  11. 2024One Prompt To Rule Them All: LLMs for Opinion Summary Evaluationcited by 16
  12. 2024A Simple but Effective Approach to Improve Structured Language Model Output for Information Extractioncited by 16
6 more
  1. 2024Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategycited by 12
  2. 2024Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Promptingcited by 10
  3. 2022Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotationscited by 9
  4. 2024Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Argumentscited by 9
  5. 2025MT-RAIG: Novel Benchmark and Evaluation Framework for Retrieval-Augmented Insight Generation over Multiple Tablescited by 7
  6. 2024Prompted Aspect Key Point Analysis for Quantitative Review Summarizationcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shoppingcited by 7
  2. 2025IPQA: A Benchmark for Core Intent Identification in Personalized Question Answeringcited by 2
  3. 2026Offline Reasoning for Efficient Recommendation: LLM-Empowered Persona-Profiled Item Indexingcited by 1