How much training data does Word2Vec need?
It sounds like Doc2Vec (or paragraph/context vectors) might be the right fit for this problem.
In a nutshell, in addition to the word vectors, you add a &quot;context vector&quot; (in your case, an embedding for the author) that is used to predict the center or context words.
This means that you would benefit from all the data about &quot;democracy&quot; but also extract an embedding for that author, which combined should allow you to analyze the bias of each author with limited data about each author.
You can use gensim's implementation. The doc includes links to the source papers.