Personalized News Categorization Through Scalable Text Classification

Existing news portals on the WWW aim to provide users with numerous articles that are categorized into specific topics. Such a categorization procedure improves presentation of the information to the end-user. We further improve usability of these systems by presenting the architecture of a personalized news classification system that exploits user’s awareness of a topic in order to classify the articles in a ‘peruser’ manner. The system’s classification procedure bases upon a new text analysis and classification technique that represents documents using the vector space representation of their sentences. Traditional ‘term-to-documents’ matrix is replaced by a ‘term-tosentences’ matrix that permits capturing more topic concepts of every document.