Sparseness-constrained Nonnegative Tensor Factorization for Detecting Topics at Different Time Scales

TL;DR

This paper introduces sparseness-constrained nonnegative tensor factorization methods that effectively detect and differentiate between long-lasting and short-term topics in temporal data, with improved control and efficiency.

Contribution

It proposes a novel sparseness-constrained NCPD approach and its online version for better detection of topics at different time scales, along with quantitative measures for topic length.

Findings

S-NCPD can automatically discover topics of varying persistence.

The online variant reduces reconstruction error faster.

Methods effectively identify short and long-term topics in real-world data.

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.