Opinion Tree Parsing for Aspect-based Sentiment Analysis

TL;DR

This paper introduces a fast, structure-aware opinion tree parsing model for aspect-based sentiment analysis that outperforms existing methods in speed and comprehensiveness by explicitly modeling sentiment element relationships.

Contribution

The paper proposes a novel opinion tree parsing approach with a context-free grammar and neural chart parser, improving speed and structure modeling in aspect-based sentiment analysis.

Findings

Our model is significantly faster than previous models.

It effectively captures comprehensive sentiment structures.

Experimental results show superior performance on benchmarks.

Abstract

Extracting sentiment elements using pre-trained generative models has recently led to large improvements in aspect-based sentiment analysis benchmarks. However, these models always need large-scale computing resources, and they also ignore explicit modeling of structure between sentiment elements. To address these challenges, we propose an opinion tree parsing model, aiming to parse all the sentiment elements from an opinion tree, which is much faster, and can explicitly reveal a more comprehensive and complete aspect-level sentiment structure. In particular, we first introduce a novel context-free opinion grammar to normalize the opinion tree structure. We then employ a neural chart-based opinion tree parser to fully explore the correlations among sentiment elements and parse them into an opinion tree structure. Extensive experiments show the superiority of our proposed model and the capacity of the opinion tree parser with the proposed context-free opinion grammar. More importantly, the results also prove that our model is much faster than previous models.