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.
M - ABSA : A Multilingual Dataset for Aspect-Based Sentiment Analysis
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Expert re-annotations of a German ABSA dataset serve as ground truth to evaluate how students, crowdworkers, and LLMs affect inter-annotator agreement and downstream performance on ACSA and TASD tasks using BERT, T5, and LLaMA models.
citing papers explorer
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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.
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Annotation Quality in Aspect-Based Sentiment Analysis: A Case Study Comparing Experts, Students, Crowdworkers, and Large Language Model
Expert re-annotations of a German ABSA dataset serve as ground truth to evaluate how students, crowdworkers, and LLMs affect inter-annotator agreement and downstream performance on ACSA and TASD tasks using BERT, T5, and LLaMA models.