Thanks for the comment, and I agree! I rephrased it a little bit to

"Empirical comparisons provide evidence that the multinomial model tends to outperform the multi-variate Bernoulli model if the vocabulary size is relatively large [13]. However, the performance of machine learning algorithms is highly dependent on the appropriate choice of features. In the case of naive Bayes classifiers and text classification, large differences in performance can be attributed to the choices of stop word removal, stemming, and token-length [14]. In practice, it is recommended that the choice between a multi-variate Bernoulli or multinomial model for text classification should precede comparative studies including different combinations of feature extraction and selection steps."