Structured sentiment analysis improves understanding of opinions in text
Structured Sentiment Analysis Using Sequence Labeling as Dependency Graph Parsing
Computation and Language
Summary
Understanding who feels what about what in sentences is a challenge in sentiment analysis. This paper presents a way to represent these detailed opinions as graphs connecting words that show feelings, targets, and people expressing them. The authors convert this graph-building problem into a simpler task where each word gets a label, making the process faster and simpler. Their method works well across multiple languages, matching more complex state-of-the-art systems.
What this means in practice
- •For natural language processing engineers: Create more efficient sentiment analysis tools that identify who feels what about what in text using simpler graph parsing methods.
- •For multilingual chatbot developers: Enhance chatbots to understand nuanced user opinions by processing sentiment structures across different languages.
Authors
Muhammad Imran, Ana Ezquerro, Carlos Gómez-Rodríguez, Anders Søgaard, David Vilares
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.