Marko Vidoni (2016) Machine learning models for predicting the volume of online news comments. EngD thesis.
Abstract
World Wide Web accompanies us on every step of our lives and we cannot anymore imagine our lives without the constant access to the internet. Publishing industry has also moved online where news articles are now published and read. The important novelty of online articles is the ability of readers to express their opinions about articles’ topics in a form of comments. It is in the best interest of web portals that the published web news are frequently commented and read, driving up the web portal visitors traffic. In this thesis a prediction system has been developed to predict the number of comments a news article in Slovene language will generate based on news text content and metadata. We got the best prediction results from bold parts of the text, coupled with metadata attributes and the extended gradient boosted regression trees model which builds separate models for each article category. Our analysis has proven that with proper data preprocessing and use of machine learning techniques it is possible to successfully predict the number of comments an article gets. In addition we also studied an influence of features on the prediction and properties of articles with good and bad prediction results.
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