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When Learning Analytics Meets MOOCs - a Review on iMooX Case Studies Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Topic Tracking in News Streams Using Latent Factor Models

Authors : Jens Meiners, Andreas Lommatzsch

Published in: Innovations for Community Services

Publisher: Springer International Publishing

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Abstract

The increasing number of published news articles and messages in social media make it hard for users to find the relevant information and to track interesting topics. Relevant news is hidden in a haystack of irrelevant data. Text-mining techniques have been developed to extract implicit, hidden information. These techniques analyze big datasets and compute “latent” features based on implicit correlations between documents and events. In this paper we develop a system that applies latent factor models on data streams. Our method allows us detecting the dominant topics and tracking the changes in the relevant topics. In addition, we explain how the extracted knowledge is used for computing recommendations based on trending topics and terms. We evaluate our system on a stream of news messages published on the micro-blogging service Twitter. The evaluation shows that our system efficiently extracts topics and provides valuable insights into the continuously changing news stream helping users quickly identifying the most relevant information as well as current trends.

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Metadata
Title
Topic Tracking in News Streams Using Latent Factor Models
Authors
Jens Meiners
Andreas Lommatzsch
Copyright Year
2016
Book
Innovations for Community Services

Print ISBN: 978-3-319-49465-4

Electronic ISBN: 978-3-319-49466-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-49466-1_12

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