Enhancing Event-Level Sentiment Analysis with Structured Arguments

TL;DR

This paper introduces a new structured approach to event-level sentiment analysis that explicitly models event arguments, demonstrating improved performance and providing a new dataset for future research.

Contribution

The paper proposes the E3SA method that explicitly extracts and models event structure information for enhanced sentiment analysis at the event level.

Findings

E3SA outperforms existing state-of-the-art methods.

A large-scale dataset with event arguments and sentiment labels is released.

Explicit modeling of event structure improves sentiment analysis accuracy.

Abstract

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}.