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

arXiv: https://arxiv.org/abs/2106.01033 · HTML: https://mlsanity.com/paper/2106.01033/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 46 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence](https://mlsanity.com/paper/1903.09588/) (2019)
- [Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis](https://mlsanity.com/paper/1701.08118/) (2017)
- [Stance Detection with Bidirectional Conditional Encoding](https://mlsanity.com/paper/1606.05464/) (2016)
- [Joint entity recognition and relation extraction as a multi-head selection problem](https://mlsanity.com/paper/1804.07847/) (2018)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [Protest Activity Detection and Perceived Violence Estimation from Social Media Images](https://mlsanity.com/paper/1709.06204/) (2017)
- [Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder](https://mlsanity.com/paper/1811.07066/) (2018)
- [Automatic Stance Detection Using End-to-End Memory Networks](https://mlsanity.com/paper/1804.07581/) (2018)
- [Relation Classification as Two-way Span-Prediction](https://mlsanity.com/paper/2010.04829/) (2020)
- [Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set](https://mlsanity.com/paper/2003.07372/) (2020)
- [Positivity Bias in Customer Satisfaction Ratings](https://mlsanity.com/paper/1803.03346/) (2018)
- [Image as Data: Automated Visual Content Analysis for Political Science](https://mlsanity.com/paper/1810.01544/) (2018)
- [Who Blames Whom in a Crisis? Detecting Blame Ties from News Articles Using Neural Networks](https://mlsanity.com/paper/1904.10637/) (2019)

## Built on by (most cited first, 4 of 4)

- [Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation](https://mlsanity.com/paper/2211.01467/) (2022)
- [Talking Point based Ideological Discourse Analysis in News Events](https://mlsanity.com/paper/2504.07400/) (2025)
- [Assessing News Thumbnail Representativeness: Counterfactual text can enhance the cross-modal matching ability](https://mlsanity.com/paper/2402.11159/) (2024)
- [Blaming Across the Aisle: Political Contrasting and Blame Attribution in the Danish Parliament](https://mlsanity.com/paper/2609.26346/) (2026)

## Abstract (arXiv)

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 construct a dataset of news sentences where sentiment relations of political entities are manually annotated. We present a simple but effective approach for utilizing a pretrained transformer, which infers the target class by predicting multiple question-answering tasks and combining the outcomes. We demonstrate the utility of our proposed method for social science research questions by analyzing positive and negative opinions between political entities in two major events: 2016 U.S. presidential election and COVID-19. The newly proposed problem, data, and method will facilitate future studies on interdisciplinary NLP methods and applications.