AUTHOR=Kassab Lara , Kryshchenko Alona , Lyu Hanbaek , Molitor Denali , Needell Deanna , Rebrova Elizaveta , Yuan Jiahong TITLE=Sparseness-constrained nonnegative tensor factorization for detecting topics at different time scales JOURNAL=Frontiers in Applied Mathematics and Statistics VOLUME=Volume 10 - 2024 YEAR=2024 URL=https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2024.1287074 DOI=10.3389/fams.2024.1287074 ISSN=2297-4687 ABSTRACT=Temporal data (such as news articles or Twitter feeds) often consists of a mixture of long-lasting trends and popular but short-lasting topics of interest. A truly successful topic modeling strategy should be able to detect both types of topics and clearly locate them in time. In this paper, we first show that nonnegative CANDECOMP/PARAFAC decomposition (NCPD) is able to discover topics of variable persistence automatically. Then, we propose sparseness-constrained NCPD (S-NCPD) and its online variant in order to actively control the length of the learned topics effectively and efficiently. Further, we propose quantitative ways to measure the topic length and demonstrate the ability of S-NCPD (as well as its online variant) to discover short and long-lasting temporal topics in a controlled manner in semi-synthetic and real-world data including news headlines. We also demonstrate that the online variant of S-NCPD reduces the reconstruction error more rapidly than S-NCPD.