| dc.description.abstract | — Latent Dirichlet Allocation (LDA) is a probabilistic
topic model that aims at organizing, visualizing, summarizing,
searching, predicting and understanding the content of any given
text data. The model enables users to discover themes in text,
annotate, organize and summarize documents. LDA inference
involves estimating the parameters and posterior distribution of a
formulated mathematical relationship. This paper investigates
topic modeling literature based on LDA and presents discoveries
and state of the art in the topic. Presented also are challenges and
popular tools. In conclusion, the paper identifies Gibbs sampling
as a popular inference mechanism and notes that the method is
limited for application in big data settings. | en_US |
