How to make an effective sampling from a database of text documents?
Off the top of my head, I would consider, at least, two approaches, as follows.


Graph sampling. If you can (and it makes sense to) model your population (database of text documents) as a graph, then consider graph sampling. Check this relevant answer of mine on the topic on Cross Validated site (contains many references - I would start with this nice overview paper).
Topic modeling. Another alternative is topic modeling (also referred to as topic models). See this introductory paper as well as this more detailed paper on the subject. Also, take a look at this interesting paper on topic modeling of streaming documents (or other situations, such as large databases, when unsupervised solutions make more sense). Finally, speaking about software for topic modeling, while many options do exist, I suggest investigating MALLET, which is an interesting Java-based software package for "statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text".