BIP! Finder - Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News Text

2021 • Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News Text

Authors: Jungseock Joo; Zhu Feng Pan; Kunwoo Park

Venue: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Type: Publication

Abstract: Understanding who blames or supports whom in news text is a critical research question in computational social science. Traditional methods and datasets for sentiment analysis are, however, not suitable for the domain of political text as they do not consider the direction of sentiments expressed between entities. In this paper, we propose a novel NLP task of identifying directed sentiment relationship between political entities from a given news document, which we call directed sentiment extraction. From a million-scale news corpus, we constru... (read more)

Impact:

3.0235012E-9 2.8226197E-9 1 1

/ Attention: 0 16

Topics: Natural language processing Information retrieval Artificial intelligence

DOI: 10.18653/v1/2021.findings-acl.358

External links: Crossref OpenAIRE

Found 2 versions BibTex PDF

Topic-specific impact indicators

Popularity:
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Influence:
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citation Count:
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Impulse:
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.