Bias detection in Philippine political news articles using SentiWordNet and inverse reinforcement model

International Conference on Information Technology and Digital Applications 2018

计算机科学;无线电电子学

Quijote, T.A.^1 ; Zamoras, A.D.^1 ; Ceniza, A.^1

Department of Computer and Information Sciences, School of Arts and Sciences, University of San Carlos, Cebu City, Philippines^1

关键词: F measure;    News articles;    Online news;    Political news;    Pre-processing;    Reinforcement model;    SentiWordNet;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/482/1/012036/pdf
DOI  :  10.1088/1757-899X/482/1/012036

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Not all information posted on the internet is deemed 'trustworthy.' Some articles, especially those related to politics, seem to display traces of bias, whether they be for or against the Philippine administration. This research aims to determine if a news article-and by extension, a news outlet-is biased based on its sentiments and use of lexica. Data were harvested from chosen websites and news outlets provided by Alexa. These data underwent pre-processing and were scored based on their sentiments with the use of SentiWordNet. The resulting scores were then fed into the Inverse Reinforcement Model, which determined whether an article is biased or not. With the use of Inquirer, Philstar, Manila Bulletin, The Manila Times, and Journal Online news articles, the system was able to detect bias with an accuracy rating of 0.89, precision of 1, recall of 0.60 and F-Measure of 0.75.