  Previous studies about event-level sentiment analysis (SA) usually model the
event as a topic, a category or target terms, while the structured arguments
(e.g., subject, object, time and location) that have potential effects on the
sentiment are not well studied. In this paper, we redefine the task as
structured event-level SA and propose an End-to-End Event-level Sentiment
Analysis ($\textit{E}^{3}\textit{SA}$) approach to solve this issue.
Specifically, we explicitly extract and model the event structure information
for enhancing event-level SA. Extensive experiments demonstrate the great
advantages of our proposed approach over the state-of-the-art methods. Noting
the lack of the dataset, we also release a large-scale real-world dataset with
event arguments and sentiment labelling for promoting more
researches\footnote{The dataset is available at
https://github.com/zhangqi-here/E3SA}.
