Large language models rate human happiness higher than humans do

Feelings are central to wellbeing, but measuring them remains difficult: surveys provide reliable data but are burdensome. LLMs introduce a provocative alternative: acting as proxies that articulate emotions and experiences on behalf of individuals. However, little work has systematically evaluated the validity of such an approach, and this is our contribution. Here, we use longitudinal smartphone data to personalise an LLM (Gemini-2.5-flash-lite) and approximate human responses to an affective survey. Using 17 weeks of multimodal sensing and questionnaires from 83 participants, we evaluate how well an LLM can predict affect across different time ranges, affect types, and individuals. Our findings reveal a systematic positivity bias: the LLM predicts people as happier than they actually report, while downplaying their negative feelings. This work highlights the importance of rigorous evaluation and affective-science grounding when using LLMs to infer human feelings from digital traces.