The biggest difference between the models you’re building from a “features” point of view is that Naive Bayes treats them as independent, whereas SVM looks at the interactions between them to a certain degree, as long as you’re using a non-linear kernel (Gaussian, rbf, poly etc.). So if you have interactions, and, given your problem, you most likely do, an SVM will be better at capturing those, hence better at the classification task you want.
The consensus for ML researchers and practitioners is that in almost all cases, the SVM is better than the Naive Bayes.
From a theoretical point of view, it is a little bit hard to compare the two methods. One is probabilistic in nature, while the second one is geometric. However, it’s quite easy to come up with a function where one has dependencies between variables which are not captured by Naive Bayes (y(a,b) = ab), so we know it isn’t an universal approximator. SVMs with the proper choice of Kernel are (as are 2/3 layer neural networks) though, so from that point of view, the theory matches the practice.
But in the end it comes down to performance on your problem – you basically want to choose the simplest method which will give good enough results for your problem and have a good enough performance. Spam detection has been famously solvable by just Naive Bayes, for example. Face recognition in images by a similar method enhanced with boosting etc.