Recently, work in NLP was initiated on a type of opinion inference that arises when opinions are expressed toward events which have positive or negative effects on entities (+/-effect events). This paper addresses methods for creating a lexicon of such events, to support such work on opinion inference. Due to significant sense ambiguity, our goal is to develop a sense-level rather than word-level lexicon. To maximize the effectiveness of different types of information, we combine a graph-based method using WordNet relations and a standard classifier using gloss information. A hybrid between the two gives the best results. Further, we provide evidence that the model is an effective way to guide manual annotation to find +/-effect senses that are not in the seed set.