I’m experimenting with an emotion-aware AI for skincare/hair struggles, curious how others handle tone detection.

I’m a solo builder and I just finished a 14-day MVP sprint for a small AI project. The idea is simple: most skincare or hair apps focus on routines, ingredients, and results But a lot of people quit because the process gets frustrating or discouraging. So I built an AI that tries to detect the user’s emotional tone before answering. If someone sounds frustrated, discouraged, or confused, the response structure changes before giving advice, for example: Instead of jumping straight to steps it might acknowledge the frustration first and then suggest adjustments. I'm currently testing whether that actually changes behavior (like whether people come back to the app after a bad skin day). A couple of people in r/SaaS suggested measuring retention specifically among high-friction users. I'm curious how other builders here approach this. If you're experimenting with tone detection, adaptive prompts, or emotion-aware AI: • Are you using sentiment analysis? • Prompt engineering only? • Or something more structured like persona memory? I’m still early and mostly experimenting, so I’d love to hear how others are building systems that adapt to user mood.