mlsanityData to 14 Oct 2026

Enhancing Event-Level Sentiment Analysis with Structured Arguments

arXiv 2205.15511 ↗First posted 31 May 2022cs.CLcited by 5

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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.

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

11 of 41 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 723
  3. 2019Entity, Relation, and Event Extraction with Contextualized Span Representationscited by 262
  4. 2020Event Extraction by Answering (Almost) Natural Questionscited by 177
  5. 2021Document-Level Event Argument Extraction by Conditional Generationcited by 155
  6. 2016Aspect Level Sentiment Classification with Deep Memory Networkcited by 82
  7. 2018Jointly Multiple Events Extraction via Attention-based Graph Information Aggregationcited by 73
  8. 2019Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extractioncited by 54
  9. 2019Attentional Encoder Network for Targeted Sentiment Classificationcited by 48
  10. 2018Transformation Networks for Target-Oriented Sentiment Classificationcited by 47
  11. 2017On the Challenges of Sentiment Analysis for Dynamic Eventscited by 4

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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 2.

5 papers.

  1. 2022A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneckcited by 10
  2. 2023EDSA-Ensemble: an Event Detection Sentiment Analysis Ensemble Architecturecited by 8
  3. 2024Deciphering Political Entity Sentiment in News with Large Language Models: Zero-Shot and Few-Shot Strategiescited by 8
  4. 2024EFSA: Towards Event-Level Financial Sentiment Analysiscited by 7
  5. 2022Causal Intervention-based Prompt Debiasing for Event Argument Extractioncited by 0