Quantum Criticism: A Tagged News Corpus Analysed for Sentiment and Named Entities
This work provides a resource for researchers and analysts to study media bias, but it is incremental as it applies existing methods to new data.
The researchers tackled the problem of analyzing news articles by creating a tagged corpus using named entity recognition and sentiment analysis at multiple levels, and they made this corpus publicly available through a web interface, with potential applications in identifying bias in news reporting.
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.