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 compare the variability of topic lengths discovered by several well-known topic modeling methods including latent Dirichlet allocation (LDA), nonnegative matrix factorization (NMF), as well as its tensor counterparts based on the nonnegative CANDECOMP/PARAFAC tensor decomposition (NCPD and Online NCPD). We demonstrate that only tensor-based methods with the dedicated mode for tracking time evolution successfully detect short-lasting topics. Furthermore, these methods are considerably more accurate in discovering the points in time when topics appeared and disappeared compared to the matrix-based methods such as LDA and NMF. We propose quantitative ways to measure the topic length and demonstrate the ability of NCPD (as well as its online variant), to discover short and long-lasting temporal topics in semi-synthetic and real-world data including news headlines and COVID-19 related tweets.

翻译：时间数据(如新闻文章或Twitter种子)往往由长期趋势与受欢迎但短期感兴趣的专题混合组成。一个真正成功的专题示范战略应该能够探测出两种类型的专题并及时明确定位。在本文中,我们比较了几个众所周知的专题模型方法发现的专题长度的变异性,包括潜在的diriclet分配(LDA)、非负矩阵因子化(NMF),以及基于非负性的CANDECOMP/PARAFAC 高频分解(NCPD和在线NPD)的对口方。我们表明,只有以专门跟踪时间演变模式为主的以虫为基础的方法才能成功发现短期专题。此外,与基于矩阵的方法(例如LDA和NMF)相比,这些方法在发现专题出现和消失的时间点方面相当准确。我们提出了衡量专题长度的量化方法,并展示NCDDD(及其在线变式)在半合成和现实世界数据(包括新闻头条和COVI-D-19相关TRV)中发现短期和长期专题的能力。