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There is currently an unprecedented demand for large-scale temporal dataanalysis due to the explosive growth of data. Dynamic topic modeling has beenwidely used in social and data sciences with the goal of learning latent topicsthat emerge, evolve, and fade over time. Previous work on dynamic topicmodeling primarily employ the method of nonnegative matrix factorization (NMF),where slices of the data tensor are each factorized into the product oflower-dimensional nonnegative matrices. With this approach, however,information contained in the temporal dimension of the data is often neglectedor underutilized. To overcome this issue, we propose instead adopting themethod of nonnegative CANDECOMP/PARAPAC (CP) tensor decomposition (NNCPD),where the data tensor is directly decomposed into a minimal sum of outerproducts of nonnegative vectors, thereby preserving the temporal information.The viability of NNCPD is demonstrated through application to both syntheticand real data, where significantly improved results are obtained compared tothose of typical NMF-based methods. The advantages of NNCPD over suchapproaches are studied and discussed. To the best of our knowledge, this is thefirst time that NNCPD has been utilized for the purpose of dynamic topicmodeling, and our findings will be transformative for both applications andfurther developments.

On Large-Scale Dynamic Topic Modeling with Nonnegative CP Tensor Decomposition