Multi-domain Multilingual Sentiment Analysis in Industry: Predicting Aspect-based Opinion Quadruples
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913943287496704 |
|---|---|
| author | White, Benjamin Shimorina, Anastasia |
| author_facet | White, Benjamin Shimorina, Anastasia |
| contents | This paper explores the design of an aspect-based sentiment analysis system using large language models (LLMs) for real-world use. We focus on quadruple opinion extraction -- identifying aspect categories, sentiment polarity, targets, and opinion expressions from text data across different domains and languages. We investigate whether a single fine-tuned model can effectively handle multiple domain-specific taxonomies simultaneously. We demonstrate that a combined multi-domain model achieves performance comparable to specialized single-domain models while reducing operational complexity. We also share lessons learned for handling non-extractive predictions and evaluating various failure modes when developing LLM-based systems for structured prediction tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10389 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-domain Multilingual Sentiment Analysis in Industry: Predicting Aspect-based Opinion Quadruples White, Benjamin Shimorina, Anastasia Computation and Language This paper explores the design of an aspect-based sentiment analysis system using large language models (LLMs) for real-world use. We focus on quadruple opinion extraction -- identifying aspect categories, sentiment polarity, targets, and opinion expressions from text data across different domains and languages. We investigate whether a single fine-tuned model can effectively handle multiple domain-specific taxonomies simultaneously. We demonstrate that a combined multi-domain model achieves performance comparable to specialized single-domain models while reducing operational complexity. We also share lessons learned for handling non-extractive predictions and evaluating various failure modes when developing LLM-based systems for structured prediction tasks. |
| title | Multi-domain Multilingual Sentiment Analysis in Industry: Predicting Aspect-based Opinion Quadruples |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.10389 |