Enhancing Event-Level Sentiment Analysis with Structured Arguments
This work addresses the problem of improving sentiment analysis accuracy for events by incorporating structured arguments, which is incremental as it builds on existing event-level SA methods.
The paper tackles event-level sentiment analysis by redefining it to incorporate structured event arguments (e.g., subject, object) and proposes an end-to-end approach (E³SA) that explicitly models this structure, achieving significant advantages over state-of-the-art methods in experiments. It also releases a large-scale real-world dataset with event arguments and sentiment labeling to facilitate further research.
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}.