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

• URL: http://arxiv.org/abs/2106.01033v1
• Date: Wed, 2 Jun 2021 09:02:14 GMT
• Title: Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News Text
• Authors: Kunwoo Park, Zhufeng Pan, and Jungseock Joo
• Abstract summary: We propose a novel NLP task of identifying directed sentiment relationship between political entities from a given news document. From a million-scale news corpus, we construct a dataset of news sentences where sentiment relations of political entities are manually annotated. 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.
• Score: 4.218255132083181
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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 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.

Related papers

• Towards Hybrid Intelligence in Journalism: Findings and Lessons Learnt from a Collaborative Analysis of Greek Political Rhetoric by ChatGPT and Humans [1.4605550954028836]
  The chapter delves into various aspects of political discourse analysis, including sentiment analysis, polarization, populism, topic detection, and Named Entities Recognition (NER) The project stands as an innovative example of human-AI collaboration within the realm of digital humanities, offering valuable insights for future initiatives.
  arXiv  Detail & Related papers  (2024-10-17T09:54:54Z)
• Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models [1.8512070255576754]
  This paper introduces a novel approach to tackle this problem by reducing the polarity of latent sentiments in news content. We employ transformation constraints to modify sentences while preserving their core semantics. Our experiments and human evaluations demonstrate the effectiveness of these two models in achieving reduced sentiment polarity with minimal modifications.
  arXiv  Detail & Related papers  (2024-02-03T13:27:32Z)
• Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framing [34.41723666603066]
  We argue that methodologies that are currently dominant fall short of addressing the complex questions and effects addressed in theoretical media studies. We discuss open questions and suggest possible directions to close identified gaps between theory and predictive models, and their evaluation.
  arXiv  Detail & Related papers  (2023-09-14T23:57:55Z)
• ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media [74.93847489218008]
  We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information. To study this task, we have proposed a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles. Our analysis demonstrates that this task is highly challenging, with large language models (LLMs) yielding unsatisfactory performance.
  arXiv  Detail & Related papers  (2023-05-23T16:40:07Z)
• Towards Corpus-Scale Discovery of Selection Biases in News Coverage: Comparing What Sources Say About Entities as a Start [65.28355014154549]
  This paper investigates the challenges of building scalable NLP systems for discovering patterns of media selection biases directly from news content in massive-scale news corpora. We show the capabilities of the framework through a case study on NELA-2020, a corpus of 1.8M news articles in English from 519 news sources worldwide.
  arXiv  Detail & Related papers  (2023-04-06T23:36:45Z)
• An Inclusive Notion of Text [69.36678873492373]
  We argue that clarity on the notion of text is crucial for reproducible and generalizable NLP. We introduce a two-tier taxonomy of linguistic and non-linguistic elements that are available in textual sources and can be used in NLP modeling.
  arXiv  Detail & Related papers  (2022-11-10T14:26:43Z)
• Revise and Resubmit: An Intertextual Model of Text-based Collaboration in Peer Review [52.359007622096684]
  Peer review is a key component of the publishing process in most fields of science. Existing NLP studies focus on the analysis of individual texts. editorial assistance often requires modeling interactions between pairs of texts.
  arXiv  Detail & Related papers  (2022-04-22T16:39:38Z)
• NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias [54.89737992911079]
  We propose a new task, a neutral summary generation from multiple news headlines of the varying political spectrum. One of the most interesting observations is that generation models can hallucinate not only factually inaccurate or unverifiable content, but also politically biased content.
  arXiv  Detail & Related papers  (2022-04-11T07:06:01Z)
• KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media [28.813287482918344]
  We propose KCD, a political perspective detection approach to enable multi-hop knowledge reasoning. Specifically, we generate random walks on external knowledge graphs and infuse them with news text representations. We then construct a heterogeneous information network to jointly model news content as well as semantic, syntactic and entity cues in news articles.
  arXiv  Detail & Related papers  (2022-04-08T13:06:09Z)
• Encoding Heterogeneous Social and Political Context for Entity Stance Prediction [7.477393857078695]
  We propose the novel task of entity stance prediction. We retrieve facts from Wikipedia about social entities regarding contemporary U.S. politics. We then annotate social entities' stances towards political ideologies with the help of domain experts.
  arXiv  Detail & Related papers  (2021-08-09T08:59:43Z)
• Sentiment Analysis Based on Deep Learning: A Comparative Study [69.09570726777817]
  The study of public opinion can provide us with valuable information. The efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems.
  arXiv  Detail & Related papers  (2020-06-05T16:28:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.