Structured Sentiment Analysis Using Sequence Labeling as Dependency Graph Parsing

2026-10-08

AI Summary

This study addresses the challenge of fine-grained sentiment analysis, which requires precisely identifying complex relationships among holders, targets, and sentiment expressions. Traditional structured parsing methods rely on intricate graph models with high computational costs. Departing from conventional paradigms, this work reformulates structured sentiment analysis as a dependency graph parsing task. It introduces a linearized graph encoding strategy that solves graph structure prediction directly through sequence labeling models, thereby circumventing complex decoding procedures. The proposed lightweight architecture enables efficient inference while achieving strong performance across five languages and seven benchmark datasets. Its results are comparable to, or even surpass, those of existing complex single-task models, offering a concise and effective unified solution for cross-lingual fine-grained sentiment analysis.

Abstract

This study addresses the problem of structured sentiment analysis, whose goal is to obtain a fine-grained sentiment graph where the nodes represent spans of sentiment holders, targets, and expressions, while the arcs define the relationships among them. Our proposed approach casts the task as dependency graph parsing, but departs from traditional parsing methods by solving it through sequence labeling. To do so, we leverage recent advances in linearized graph encodings that allow each word in the input to be assigned a label, effectively capturing the structure of the dependency graph. We conducted experiments on seven datasets spanning five languages (English, Spanish, Norwegian, Basque, and Catalan), showing performance competitive with leading, more complex single-model approaches.

Problem

• Structured Sentiment Analysis
• Sentiment Graph
• Dependency Graph Parsing
• Sequence Labeling

Innovation

• Structured Sentiment Analysis
• Dependency Graph Parsing
• Sequence Labeling
• Linearized Graph Encodings
• Fine-grained Sentiment Graph