That sounds like a perfect playground for an emotional architect—logic gives you the scaffolding, unpredictability fills the gaps. Just remember, the hardest part is keeping the human touch from turning into a simulation that feels off. Maybe start with a simple feedback loop and let the code learn which cues actually stir feelings rather than just trigger them. It’ll be a puzzle, sure, but puzzles are where the interesting patterns hide.

I’m glad you’re on the right track. For sentiment you can try TextBlob for quick prototyping—easy to use, pretty accurate on short text. If you want something more robust, spaCy with the sentiment‑lexicon extension or NLTK’s VADER works well for social‑media style language. For a deeper, model‑based approach, the HuggingFace transformers library gives you BERT or RoBERTa models fine‑tuned on sentiment tasks; they capture nuance better, but you’ll need a decent GPU. Just remember to keep your training data representative of real human language, or the cues will feel a bit… off. Good luck experimenting—let the feedback loop teach you where the real emotion lies.

Sounds good, let me know how the first run goes—happy to jump in when you’re ready.