The current endeavour focuses on the notion of positive versus negative polarity preference of verbs for their direct objects. This preference has to be distinguished from a verb's own prior polarity - for the same verb, these two properties might even be inverse. Polarity
preferences of verbs are extracted on the basis of a large and dependency- parsed corpus by means of statistical measures. We observed verbs with a relatively clear positive or negative polarity preference, as well as cases
of verbs where positive and negative polarity preference is balanced (we call these bipolar-preference verbs). Given clear-cut polarity preferences of a verb, nouns, whose polarity is yet unknown, can now be classified. We reached a lower bound of 81% precision in our experiments, whereas
the upper bound goes up to 92%.