Machine Learning

Definitely one tricky part as you mentioned is the dataset. In an ideal world, you'll have a supervised dataset of (document, personality type) pairs and you can train a model on these (just like u/Veggies-are-okay mentioned).

Assuming you don't have this data, a couple options:

• Make the data. Some quick google searches show that many celebrities do have known Big-5s. You could manually curate Big-5s and text written by these celebrities to build these pairs.
• Use synthetic data. Try asking an LLM (like ChatGPT) to write a text on a random topic as if they were $RANDOM big-5 then just use these results as your training pairs.
• Try clustering. Potentially similar personality types have similar embeddings. Take a dataset of writings, embed them using something like BERT, label/best-effort-guess a few and then predict personalities based on the proximity of a piece of known big-5 text in the embedding space. You could extend this to training a model that asks "do text A and text B display the same big-5" which could potentially be an easier problem to get samples for and then run this model against a set of know big-5s and your unknown example.
• Use a proxy. There might be datasets/models out there that predict heuristics that could be combined to find big 5. Like maybe a sentiment score is correlated with agreeableness. Potentially you might be able to create word/phrase banks such that using certain phrases is potentially indicative of a leaning on big-5 ("has_neurotic_phrases" is then a feature in your model)