How to classify support call texts?
Rather than use an external dictionary of keywords that are indicative of target class, you may want to take your raw data (or rather a random subset of it), then hand-label your instances (assign each row a label, BEST_EFFORT or URGENT). This becomes your training data - each row of data can be transformed into a bag-of-words vector indicating the presence/absence of the word in that particular text. You can train a classifier on this data, for example a naive bayes classifier, which can then be tested on the held out unseen test data. The advantages of the  proposed approach are: (1) automated computation of features vs. hand created dictionary; (2) probabilistic/weighted indicators of class vs. binary dictionary indicators.