Opinion Extraction as A Structured Sentiment Analysis using Transformers

TL;DR

This paper proposes a unified transformer-based model for extracting opinion tuples, including holders, targets, and expressions, from sentences to enhance structured sentiment analysis.

Contribution

It introduces a novel approach that combines relationship extraction and named entity recognition into a single model for opinion tuple extraction.

Findings

Effective extraction of multiple opinion tuples from single sentences.

Unified model improves efficiency over separate tasks.

Experimental results demonstrate improved accuracy.

Abstract

Relationship extraction and named entity recognition have always been considered as two distinct tasks that require different input data, labels, and models. However, both are essential for structured sentiment analysis. We believe that both tasks can be combined into a single stacked model with the same input data. We performed different experiments to find the best model to extract multiple opinion tuples from a single sentence. The opinion tuples will consist of holders, targets, and expressions. With the opinion tuples, we will be able to extract the relationship we need.