Can I create multiple models using an attribute as a loop variable?
I have a data set that is comprised of messages from dozens of different authors. My goal is to develop a model based on multiple attributes (including TF-IDF) of each author's messages. Since each author's messages are likely to be unique in terms of their content, topics, word usage, etc., I'd like to develop one model for each author. In other words, if I have 10 authors, I want to create 10 unique models (one for each author's messages). Thus, I have several questions:
1) One of the attributes of my data is the author's name. Can I use this attribute somehow as a loop variable so that for each iteration of the loop I can analyze all of an author's messages and train and create a model unique to that author?
2) How can I name and store these models in such a way that in another RM process I can retrieve a model based on an author's name? In other words, if I train a model based on messages whose author is Jenny, then how can I retrieve and apply "Jenny's model" if I get new messages from Jenny in the future (or "Steve's model" if I get new messages from Steve, and so on)?
3) Also, is there an unsupervised model that can be used to model all of an individual author's messages as a single class, and then apply the model to future messages to detect deviations or anomalies?