I'm creating a naive bayes text classifier, but I'm wondering if it's a good idea to break the text up into both unigrams and bigrams. Should I only use one method? Will having both variations mess with the algorithm?
Having a variation is a very common practice. It actually has a smoothing effect.
Bigram --> Low bias/high variance Unigram --> High bias/low variance.
Combination the two helps to "hedge" the bets made between the two. See for instance, the first equation on pg. 13 of this link. The author shows how to merge trigram, bigram and unigram estimates
Starting simple with just unigrams and then just bigrams would likely be ideal. If you use both then the complexity of the model would increase, and it might not even result in an increase in performance. If you do include both unigrams and bigrams, and that results in a better classifier, I don't see why not!