Inducing the contextual and prior polarity of nouns from the induced polarity preference of verbs
The current endeavour focuses on the notion of positive versus negative polarity preferences of verbs for their direct objects. We observed verbs with a relatively clear positive or negative polarity preference (called polar), as well as cases of verbs where positive and negative polarity preference is balanced (called bi-polar). These polarity preferences of verbs are induced on the basis of a large dependency-parsed corpus by means of statistical measures and a lexicon of manually curated prior noun polarities. Given (learned) polar verbs, the contextual polarity of their direct objects can be derived. We reached a lower bound of 81.97% and an upper bound of 93.34% precision in these experiments. The polarity of a noun was predicted by the majority vote of the verbs that take that noun as its direct object in our corpus. In a second experimental setting, 1 we also considered the role of neutral nouns co-occurring with these verbs. We found that the induction of the (tripartite) prior polarity of nouns can be achieved with a precision of 75.97%.
sentiment analysis polarity inference polarity lexicon