“In recent years, many researchers have recognized the importance of associating events withsentiments. Previous approaches focus on generalizing events and extracting sentimental in-formation from a large-scale corpus. However, since context is absent and sentiment is oftenimplicit in the event, these methods are limited in comprehending the semantics of the eventand capturing effective sentimental clues. In this work, we propose a novel Multi-perspectiveKnowledge-injected Interaction Network (MKIN) to fully understand the event and accuratelypredict its sentiment by injecting multi-perspective knowledge. Specifically, we leverage con-texts to provide sufficient semantic information and perform context modeling to capture thesemantic relationships between events and contexts. Moreover, we also introduce human emo-tional feedback and sentiment-related concepts to provide explicit sentimental clues from theperspective of human emotional state and word meaning, filling the reasoning gap in the senti-ment prediction process. Experimental results on the gold standard dataset show that our modelachieves better performance over the baseline models.”