| Challenge: | Existing studies on emotional support detection focus on the presence or absence of emotional support, while the available datasets are limited or scarce in terms of size. |
| Approach: | They propose to use a dataset of 6,500 sentences annotated with encouragement and sympathy to train BERT-based classifiers on this dataset and apply their best BERT model to two large scale experiments. |
| Outcome: | The proposed model improves the emotional state of users while the lack of emotional support negatively impacts patients’ emotional state. |
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| Challenge: | Existing studies on empathy and mental health-related corpora focus on broader contexts and lack domain specificity. |
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CancerEmo: A Dataset for Fine-Grained Emotion Detection (2020.emnlp-main)
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| Challenge: | a lack of large annotated datasets hinders emotion detection in the health domain . a recent study shows that online sharing of emotions is beneficial to a patient's progress . |
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A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support (2020.emnlp-main)
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Emotion-Infused Models for Explainable Psychological Stress Detection (2021.naacl-main)
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Condolence and Empathy in Online Communities (2020.emnlp-main)
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| Challenge: | Using computational tools, we examine the dynamics of condolence online. |
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Causal-ESC: Reliable Policy Learning for Emotional Support Conversation via Causal Inference (2026.acl-long)
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| Challenge: | Existing approaches to Emotional Support Conversation (ESC) are mechanistically opaque and lacks a causal mechanism between dialogue features and effective empathic strategies. |
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