Quantum Criticism: A Tagged News Corpus Analysed for Sentiment and Named Entities

TL;DR

This paper presents a new annotated news corpus with sentiment and named entity tags, derived from RSS feeds, to facilitate bias detection and analysis in news reporting.

Contribution

It introduces a publicly available tagged news corpus with sentiment and entity annotations, applying multiple NER tools and sentiment analysis at various levels.

Findings

Multiple NER tools evaluated for success

Sentiment analysis performed at document, paragraph, and sentence levels

Corpus enables bias detection in news reporting

Abstract

In this research, we continuously collect data from the RSS feeds of traditional news sources. We apply several pre-trained implementations of named entity recognition (NER) tools, quantifying the success of each implementation. We also perform sentiment analysis of each news article at the document, paragraph and sentence level, with the goal of creating a corpus of tagged news articles that is made available to the public through a web interface. Finally, we show how the data in this corpus could be used to identify bias in news reporting.