# How to make an effective sampling from a database of text documents?

Problem: I want to know methods to perform an effective sampling from a database. The size of the database is about 250K text documents and in this case each text document is related to some majors (Electrical Engineering, Medicine and so on). So far I have seen some simple techniques like Simple random sample and Stratified sampling; however, I don't think it's a good idea to apply them for the following reasons:

• In the case of simple random sample, for instance, there are a few documents in the database that talk about majors like Naval Engineering or Arts. Therefore I think they are less likely to be sampled with this method but I would like to have some samples of every major as possible.

• In the case of stratified sampling, most of the documents talk about more than one major so I cannot divide the database in subgroups because they will not be mutually exclusive, at least for the case in which each major is a subgroup.

Finally, I cannot use the whole database due to expensive computational cost processing. So I would really appreciate any suggestions on other sampling methods. Thanks for any help in advance.