Nicolay Rusnachenko

And to clarify your findings on those words you can measure such degree with tf-idf application for your annotated texts. Basically, if you have a set of positive and negative responses from GPT-4o, you can calculate so-called Semantic Orientation (SO) based on Pointwise Mutual Information (PMI). This would give a consistecy to your observations.
This comes from the relatively old classics: https://arxiv.org/pdf/cs/0212032

Oh, that sound interesting and looks like your focus are patients then, while mine majorly was mass-media (authors) and dialogues (character conversations).

To make sure I understood you correctly frames are basically describing how a sentiment is related to entities in a sentence—is this a roughly correct understanding?

That's right, so it acts as a word that connects several parties (including entities), that are scientifically declared as "roles" with the polarity score ("positive", "negative"). So that in your case "sounds like", "rough", "tough" could be treated as negative by GPT-4o with respect to the topic of the question.

As for the frames, here is might be more general definition you might be interested to check (see diagram):
https://aclanthology.org/D18-2008.pdf
The concept is the same, while and instead of words they refer to them as triggers.

Thank you @ychen for sharing this! I was curious, because the word freq analysis you're attempted to do is very aligned with lexicons construction and frames in the domain of sentiment analysis. In particular, this could be enhanced up to analysis on a specific set of words, usually dubbed as frames. So and unlike just words, frames goes further with sentiment of subject towards objects.

FYI. We cover the similar for news and domain specific (Russian language) here: https://github.com/nicolay-r/RuSentiFrames

Thanks! Any publicly available resources of such a synthetic texts that would lead to your observations?