EmBot combines wearable-triggered stress detection with LLM conversational support and was probed via expert interviews to surface design considerations for daily stress management.
Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems , articleno =
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
LLMs achieve Pearson correlations up to 0.97 and 94% classification accuracy on product desirability sentiment from qualitative data, outperforming lexicon and transformer baselines while providing confidence ratings and rationales.
citing papers explorer
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Exploring Expert Perspectives on Wearable-Triggered LLM Conversational Support for Daily Stress Management
EmBot combines wearable-triggered stress detection with LLM conversational support and was probed via expert interviews to surface design considerations for daily stress management.
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Evaluating LLM Usage for Efficient and Explainable Numerical and Classified Implicit Sentiment Analysis of Product Desirability
LLMs achieve Pearson correlations up to 0.97 and 94% classification accuracy on product desirability sentiment from qualitative data, outperforming lexicon and transformer baselines while providing confidence ratings and rationales.