Towards Target-dependent Sentiment Classification in News Articles
- URL: http://arxiv.org/abs/2105.09660v1
- Date: Thu, 20 May 2021 10:48:03 GMT
- Title: Towards Target-dependent Sentiment Classification in News Articles
- Authors: Felix Hamborg and Karsten Donnay and Bela Gipp
- Abstract summary: This article introduces NewsTSC, a manually annotated dataset to explore TSC on news articles.
We find that sentiment in the news is expressed less explicitly, is more dependent on context and readership, and requires a greater degree of interpretation.
Reasons include incorrectly resolved relation of target and sentiment-bearing phrases and off-context dependence.
- Score: 10.541787182702217
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Extensive research on target-dependent sentiment classification (TSC) has led
to strong classification performances in domains where authors tend to
explicitly express sentiment about specific entities or topics, such as in
reviews or on social media. We investigate TSC in news articles, a much less
researched domain, despite the importance of news as an essential information
source in individual and societal decision making. This article introduces
NewsTSC, a manually annotated dataset to explore TSC on news articles.
Investigating characteristics of sentiment in news and contrasting them to
popular TSC domains, we find that sentiment in the news is expressed less
explicitly, is more dependent on context and readership, and requires a greater
degree of interpretation. In an extensive evaluation, we find that the state of
the art in TSC performs worse on news articles than on other domains (average
recall AvgRec = 69.8 on NewsTSC compared to AvgRev = [75.6, 82.2] on
established TSC datasets). Reasons include incorrectly resolved relation of
target and sentiment-bearing phrases and off-context dependence. As a major
improvement over previous news TSC, we find that BERT's natural language
understanding capabilities capture the less explicit sentiment used in news
articles.
Related papers
- A Multilingual Similarity Dataset for News Article Frame [14.977682986280998]
We introduce an extended version of a large labeled news article dataset with 16,687 new labeled pairs.
Our method frees the work of manual identification of frame classes in traditional news frame analysis studies.
Overall we introduce the most extensive cross-lingual news article similarity dataset available to date with 26,555 labeled news article pairs across 10 languages.
arXiv Detail & Related papers (2024-05-22T01:01:04Z) - SCStory: Self-supervised and Continual Online Story Discovery [53.72745249384159]
SCStory helps people digest rapidly published news article streams in real-time without human annotations.
SCStory employs self-supervised and continual learning with a novel idea of story-indicative adaptive modeling of news article streams.
arXiv Detail & Related papers (2023-11-27T04:50:01Z) - From Nuisance to News Sense: Augmenting the News with Cross-Document
Evidence and Context [25.870137795858522]
We present NEWSSENSE, a novel sensemaking tool and reading interface designed to collect and integrate information from multiple news articles on a central topic.
NEWSSENSE augments a central, grounding article of the user's choice by linking it to related articles from different sources.
Our pilot study shows that NEWSSENSE has the potential to help users identify key information, verify the credibility of news articles, and explore different perspectives.
arXiv Detail & Related papers (2023-10-06T21:15:11Z) - Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News
Detection [50.07850264495737]
"Prompt-and-Align" (P&A) is a novel prompt-based paradigm for few-shot fake news detection.
We show that P&A sets new states-of-the-art for few-shot fake news detection performance by significant margins.
arXiv Detail & Related papers (2023-09-28T13:19:43Z) - 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) - Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S.
News Headlines [63.52264764099532]
We use a large dataset of 1.8 million news headlines from major U.S. media outlets spanning from 2014 to 2022.
We quantify the fine-grained thematic discrepancy related to four prominent topics - domestic politics, economic issues, social issues, and foreign affairs.
Our findings indicate that on domestic politics and social issues, the discrepancy can be attributed to a certain degree of media bias.
arXiv Detail & Related papers (2023-03-28T03:31:37Z) - Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases.
We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions.
Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
arXiv Detail & Related papers (2023-01-14T18:58:42Z) - Nothing Stands Alone: Relational Fake News Detection with Hypergraph
Neural Networks [49.29141811578359]
We propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism.
Our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.
arXiv Detail & Related papers (2022-12-24T00:19:32Z) - Headline Diagnosis: Manipulation of Content Farm Headlines [0.0]
It is essential to accurately predict whether a news article is from official news agencies.
This work develops a headline classification based on Convoluted Neural Network to determine credibility of a news article.
arXiv Detail & Related papers (2022-04-25T02:55:33Z) - Newsalyze: Enabling News Consumers to Understand Media Bias [7.652448987187803]
Knowing a news article's slant and authenticity is of crucial importance in times of "fake news"
We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, named bias by word choice and labeling (WCL)
WCL bias can alter the assessment of entities reported in the news, e.g., "freedom fighters" vs. "terrorists"
arXiv Detail & Related papers (2021-05-20T11:20:37Z) - Context in Informational Bias Detection [4.386026071380442]
We explore four kinds of context for informational bias in English news articles.
We find that integrating event context improves classification performance over a very strong baseline.
We find that the best-performing context-inclusive model outperforms the baseline on longer sentences.
arXiv Detail & Related papers (2020-12-03T15:50:20Z)
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.