Modeling Complex Interactions in Long Documents for Aspect-Based Sentiment Analysis
Zehong Yan1, Wynne Hsu1, Mong Li Lee1, David Roy Bartram-Shaw2
1NUS Centre for Trusted Internet & Community, National University of Singapore
2Edelman Data & Intelligence
WASSA Workshop, ACL, 2024 / PDF / Project Page / Code / Data
We introduce DART, a hierarchical transformer-based framework for aspect-based sentiment analysis in long documents. DART handles the complexities of longer text through its global context interaction and two-level aspect-specific aggregation blocks. For empirical validation, we curate two datasets for aspect-based sentiment analysis in long documents: SocialNews and TrustData.