Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.
Treating mutual-information subjects imply The use of descriptive-statistics, and this tag will draw more attention to the niche talented statisticians to solve this challenge.
In this case I think you can use Use the tfidf. rank_document() function to score documents based on overlapping content or Use the docsim. DocSim() class to score documents on similarity using doc2vec and the GloVe word embedding model.
As I have explained I only have the "Good" documents, so that will not solve the issue, and that's why I was asking about an approach maybe for one-class classification using NLP. And labeling years of long articles by hand is far fetched.
