I'm currently working on a school project to classify abstracts from papers in to 4 categories. Currently Bernoulli Naive Bayes scores 85% accuracy. For comparison kNN scores about the same.
One think about naive Bayes and kNN: Although the performance may be similar, I would consider Naive beneficial if the data set is large. kNN requires to re-evaluate the data. Naive Bayes makes the classification computationally cheaper and with "on-line" learning, you can still improve or adjust it over time