Ranking of comments published on YouTube using natural language processing techniques
Journal Title: Revista Romana de Interactiune Om-Calculator - Year 2015, Vol 8, Issue 3
Abstract
Publishing of comments is a form of interaction that has become an important part of the Social Web phenomenon. Comments are enhancing online interaction, however their visualization and ranking becomes extremely difficult or even impossible for videos or other types of online articles that have millions of comments linked to them. This paper presents a method to identify the most relevant comments published for a video uploaded on the YouTube video-sharing platform, offering at the same time a detailed analysis of the features that are relevant for the most important comments linked to an article. The analysis we have performed shows that videos from different categories have different relevant attributes and there are important differences between these attributes for specific pairs of categories (e.g. Music vs. Education).
Authors and Affiliations
Iulian Radu, Traian Eugen Rebedea
Keywords
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