Work

Subjectivity Detection in English News using Large Language Models

Year: 2024

Type: conference-paper

Abstract: Trust in media has reached a historical low as consumers increasingly doubt the credibility of the news they encounter.This growing skepticism is exacerbated by the prevalence of opinion-driven articles, which can influence readers' beliefs to align with the authors' viewpoints.In response to this trend, this study examines the expression of opinions in news by detecting subjective and objective language.We conduct an analysis of the subjectivity present in various news datasets and evaluate how different language models detect subjectivity and generalize to out-of-distribution data.We also investigate the use of in-context learning (ICL) within large language models (LLMs) and propose a straightforward prompting method that outperforms standard ICL and chain-of-thought (CoT) prompts.

Source: Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA)

DOI: 10.18653/v1/2024.wassa-1.17

Authors: Mohammad Shokri, Vivek Sharma, Elena Filatova, Shweta Jain, Sarah Levitan

Institutions: The Graduate Center, CUNY, City University of New York, Hunter College

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Science Foundation, Purdue University

Award: Category I: Anvil - A National Composable Advanced Computational Resource for the Future of Science and Engineering