I'm working on text classification and I have a set of 200.000 tweets.

The idea is to manually label a short set of tweets and train classifiers to predict the labels of the rest. Supervised learning.

What I would like to know is if there is a method to choose what samples to include in the train set in a way that this train set is a good representation of the whole data set, and because the high diversity included in the train set, the trained classifiers have considerable trust to be applied on the rest of tweets.