Online news topic detection and tracking via localized feature selection

The detection of topic trends has increasingly attracted interest over the past decades, fueled in particular by the revolution of internet and the emergence of social media. However, manual topic detection and tracking (TDT) is not efficient, this has become possible thanks to the development of modern data mining techniques and their flexibility to model potential issues. A critical challenge in this context is the representation choices of news stories along with adequate detection of new topics. To this end, we propose a unified statistical framework that allows simultaneous topic clustering and feature (word) selection in online settings based on spherical mixtures. Through empirical experiments, the proposed framework demonstrates the ability to learn new topics incrementally and improve detection quality within a reasonable time framework on diverse high-dimensional datasets.