论文标题
通过结构化参数增强事件级别的情感分析
Enhancing Event-Level Sentiment Analysis with Structured Arguments
论文作者
论文摘要
关于事件级别情绪分析（SA）的先前研究通常将事件作为主题，类别或目标术语建模，而对情感的潜在影响的结构化参数（例如，受试者，对象，时间和位置）并没有很好地研究。在本文中，我们将任务重新定义为结构化事件级别的SA，并提出了端到端事件级别的情感分析（$ \ textit {e}^{3} {3} \ textit {sa} $）解决此问题。具体而言，我们明确提取并建模事件结构信息，以增强事件级别的SA。广泛的实验证明了我们提出的方法比最先进的方法具有很大的优势。注意到缺乏数据集，我们还发布了一个大规模的现实世界数据集，其中包含事件参数和情感标签，以促进更多研究\ footNote {数据集{该数据集可在https://github.com/zhangqi-here/e3sa}上找到。
Previous studies about event-level sentiment analysis (SA) usually model the event as a topic, a category or target terms, while the structured arguments (e.g., subject, object, time and location) that have potential effects on the sentiment are not well studied. In this paper, we redefine the task as structured event-level SA and propose an End-to-End Event-level Sentiment Analysis ($\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}.
