  Recognizing affective events that trigger positive or negative sentiment has
a wide range of natural language processing applications but remains a
challenging problem mainly because the polarity of an event is not necessarily
predictable from its constituent words. In this paper, we propose to propagate
affective polarity using discourse relations. Our method is simple and only
requires a very small seed lexicon and a large raw corpus. Our experiments
using Japanese data show that our method learns affective events effectively
without manually labeled data. It also improves supervised learning results
when labeled data are small.
