# Enhancing Event-Level Sentiment Analysis with Structured Arguments

arXiv: https://arxiv.org/abs/2205.15511 · HTML: https://mlsanity.com/paper/2205.15511/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 31 May 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021)
- [Aspect Level Sentiment Classification with Deep Memory Network](https://mlsanity.com/paper/1605.08900/) (2016)
- [Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://mlsanity.com/paper/1809.09078/) (2018)
- [Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://mlsanity.com/paper/1904.07535/) (2019)
- [Attentional Encoder Network for Targeted Sentiment Classification](https://mlsanity.com/paper/1902.09314/) (2019)
- [Transformation Networks for Target-Oriented Sentiment Classification](https://mlsanity.com/paper/1805.01086/) (2018)
- [On the Challenges of Sentiment Analysis for Dynamic Events](https://mlsanity.com/paper/1710.02514/) (2017)

## Built on by (most cited first, 5 of 5)

- [A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck](https://mlsanity.com/paper/2208.13017/) (2022)
- [EDSA-Ensemble: an Event Detection Sentiment Analysis Ensemble Architecture](https://mlsanity.com/paper/2301.12805/) (2023)
- [Deciphering Political Entity Sentiment in News with Large Language Models: Zero-Shot and Few-Shot Strategies](https://mlsanity.com/paper/2404.04361/) (2024)
- [EFSA: Towards Event-Level Financial Sentiment Analysis](https://mlsanity.com/paper/2404.08681/) (2024)
- [Causal Intervention-based Prompt Debiasing for Event Argument Extraction](https://mlsanity.com/paper/2210.01561/) (2022)

## Abstract (arXiv)

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 (textitE3textitSA) 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 researchesfootnoteThe dataset is available at https://github.com/zhangqi-here/E3SA.