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arXiv · 2505.10389

Multi-domain Multilingual Sentiment Analysis in Industry: Predicting Aspect-based Opinion Quadruples

Benjamin White·Anastasia Shimorina
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Abstract

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.

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Benjamin White, Anastasia Shimorina. 2025-05-15. Multi-domain Multilingual Sentiment Analysis in Industry: Predicting Aspect-based Opinion Quadruples. https://arxiv.org/abs/2505.10389

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