Human emotional intelligence depends on context, experience, empathy and social understanding. AI works differently. We are interested in where these two systems overlap, where they differ, and how that knowledge can improve communication.
How people recognize tone, context, humor, uncertainty, sadness, warmth and other emotional signals in communication.
How models identify emotional patterns, reason about context and choose responses that are useful without pretending to feel human emotions.
Can AI move beyond positive or negative sentiment and understand why a person may sound frustrated, sad, uncertain or excited?
Can AI understand why something is funny, recognize irony, and distinguish playful language from hostility?
How can AI respond to emotional signals in a way that makes communication clearer, calmer and more comfortable?
How can emotion analysis remain useful while respecting uncertainty, privacy and the limits of what AI can truly infer?
These papers are not authored by EmoScan. They are public academic works we find relevant to understanding emotional intelligence, affective computing and human–AI interaction.
A psychometric evaluation of emotion understanding in large language models, including comparisons with human responses and an examination of whether similar scores reflect similar underlying mechanisms.
Read on arXiv ↗Introduces FACET, a framework that separates emotional perception, reasoning and interaction, showing that strength in recognizing emotion does not always translate into effective emotional responses.
Read on arXiv ↗A broad review of more than 320 studies on generative approaches to emotion recognition across text, speech, facial imagery and physiological signals.
Read on arXiv ↗People use emotional analysis with messages, resumes, reviews and other information that may be personal. We treat privacy and data minimization as core product principles, not optional features.
Our goal is to turn better understanding of emotional communication into tools that help people communicate more clearly.
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