Enhancing Event-Level Sentiment Analysis with Structured Arguments

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...

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Bibliographic Details
Published in:arXiv.org
Main Authors: Zhang, Qi, Zhou, Jie, Chen, Qin, Bai, Qinchun, He, Liang
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 31.05.2022
Subjects:
Data mining
Datasets
Sentiment analysis
ISSN:2331-8422
Online Access:Get full text
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Summary: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}.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
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ISSN:2331-8422
DOI:10.48550/arxiv.2205.15511