News objectivity classification using linguistic features and machine learning models - Edrosa, Loraine Corese T.
The internet is becoming more popular as a news source for many individuals because of its convenience. A lot of online news providers have become available because of this growing demand, and with this also comes the threat of misinformation. It has become a challenge to identify the credibility of such sources as well as the quality and objectivity of the content they provide. There are established news sources that are already well-known for their credibility, but articles tend to become decontextualized from their sources when social media gets into play. This research aims to develop an automated objectivity classification for online news articles using combinations of linguistic and machine learning tools. We conducted a series of experiments using different datasets retrieved from multiple sources, certain feature extraction methods such as term frequency - inverse document frequency (TFIDF) and Linguistic Inquiry and Word Count (LIWC), and machine learning models including support vector machines (SVM) and random forests. We were able to achieve a classification accuracy of 93.4 percent, and gained a number of valuable insights on the linguistic feature extraction methods and machine learning models we used.