PERCEIVE is the first bilingual benchmark integrating author content, reader emotions from comments, communication behavior, user attributes, and social graphs for personalized social media emotion understanding.
DAGCN : Distance-based and Aspect-oriented Graph Convolutional Network for Aspect-based Sentiment Analysis
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
GHI introduces an incidence-based structural reasoning layer using Graphormer on conditioned hypergraphs for ABSA, reporting outperformance on SemEval benchmarks, near-parity with 11B models at 247M parameters, and robustness on ARTS.
DABS is a single-pass framework that builds a depth-ordered substrate from one Transformer encoding and performs lightweight aspect-conditioned readout, cutting computation by up to 60% on multi-aspect ATSA benchmarks while matching prior accuracy.
citing papers explorer
-
PERCEIVE: A Benchmark for Personalized Emotion and Communication Behavior Understanding on Social Media
PERCEIVE is the first bilingual benchmark integrating author content, reader emotions from comments, communication behavior, user attributes, and social graphs for personalized social media emotion understanding.
-
GHI: Graphormer over Conditioned Hypergraph Incidence for Aspect-Based Sentiment Analysis
GHI introduces an incidence-based structural reasoning layer using Graphormer on conditioned hypergraphs for ABSA, reporting outperformance on SemEval benchmarks, near-parity with 11B models at 247M parameters, and robustness on ARTS.
-
Single-Pass, Depth-Selective Reading for Multi-Aspect Sentiment Analysis
DABS is a single-pass framework that builds a depth-ordered substrate from one Transformer encoding and performs lightweight aspect-conditioned readout, cutting computation by up to 60% on multi-aspect ATSA benchmarks while matching prior accuracy.