Datasets:
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This is the EXPRESS dataset from the paper:
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https://arxiv.org/pdf/2509.09593
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## Overview
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EXPRESS (EXperiences and PRocessed Emotions in Self-disclosure Stories) is a benchmark dataset for evaluating fine-grained emotion recognition in language models. It contains 33,679 naturally occurring Reddit-based human experiences paired with self-disclosed emotion labels.
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EXPRESS uses emotions explicitly disclosed by the original authors as ground-truth labels. The dataset covers 251 fine-grained emotion labels and an average context length of 259 words, enabling evaluation of models’ ability to infer nuanced emotional states from rich real-world contexts.
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This is the EXPRESS dataset from the paper:
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https://arxiv.org/pdf/2509.09593
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EXPRESS (EXperiences and PRocessed Emotions in Self-disclosure Stories) is a benchmark dataset for evaluating fine-grained emotion recognition in language models. It contains 33,679 naturally occurring Reddit-based human experiences paired with self-disclosed emotion labels.
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EXPRESS uses emotions explicitly disclosed by the original authors as ground-truth labels. The dataset covers 251 fine-grained emotion labels and an average context length of 259 words, enabling evaluation of models’ ability to infer nuanced emotional states from rich real-world contexts.
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