There are many other factors... but an important one (in determining a suitable ratio and volume of training data) is the expected distribution of each message category (Positive, Neutral, Negative) in the real world. Effectively, a good baseline for the training set (and the control set) is
- [qualitatively] as representative as possible of the whole "population"
- [quantitatively] big enough that measurements made from such sets is statistically significant.
The effect of the [relative] abundance of a certain category of messages in the training set is hard to determine; it is in any case a lesser factor -or rather one that is highly sensitive to- other factors. Improvements in the accuracy of the classifier, as a whole, or with regards to a particular category, is typically tied more to the specific implementation of the classifier (eg. is it Bayesian, what are the tokens, are noise token eliminated, is proximity a factor, are we using bi-grams etc...) than to purely quantitative characteristics of the training set.
While the above is generally factual but moderately helpful for the selection of the training set's size and composition, there are ways of determining, post facto, when an adequate size and composition of training data has been supplied.
One way to achieve this is to introduce a control set, i.e. one manually labeled but that is not part of the training set and to measure for different test runs with various subsets of the training set, the recall and precision obtained for each category (or some similar accuracy measurements), for this the classification of the control set. When these measurements do not improve or degrade, beyond what's statistically representative, the size and composition of the training [sub-]set is probably the right one (unless it is an over-fitting set :-(, but that's another issue altogether... )
This approach, implies that one uses a training set that could be 3 to 5 times the size of the training subset effectively needed, so that one can build, randomly (within each category), many different subsets for the various tests.