welcome to the project page for spectral.ly. spectra.ly is an app that is meant to identify bias in the news media, using tfidf and support vector classification.
there are 1million and 1 ways to look at any news-worthy event that happens. and the numerous number of articles and outlets out there all report on that event in a way that is relevant to their audience. often it is difficult to determine what these biases might be without reading the article first.
spectral.ly collects and analyzes articles related to a user-specified news event, and provides a ranking of all articles on the event based on: a) how much persuasive language is being used (overall bias) b) classification as a liberal or conservative view point
while there are numerous other forms of perspective (international vs domestic; mainstream vs non-mainstream; etc.), the ones mentioned above address the two most common forms of perspective that readers are interested in.
for the analysis of persuasive language, word frequencies on wikipedia were compared to common english-language word frequencies (wordcount.org). any word that was much less likely to appear on wikipedia was considered a 'biased' word.
for the liberal/conservative classification, i trained a support vector classifier on 6 of the most liberal and conservative blogs online. i achieved an 80% accuracy through this classifier compared to others.
(edit: the pickle of the SVC classifier is large, and is not in this github repo. please message me if you want to gain access to it.)
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