Trending Research Topics Detection over Time Using the Latent Dirichlet Allocation Model.

Topic modeling has become an extraordinary tool for analyzing large, unclassified documents to detect patterns that use similar words. It also permits the discovery of hidden themes that pervade the collection, allowing annotation according to those themes, and then using these to summarize and search the text. This paper focuses on Latent Dirichlet Allocation, which is one of the most widely used methods for topic modeling. Trend detection and evolution of topics relating to research topics was attempted from the Journal of the Modern Language Association of America. The study also identified those documents that explained topics according to time series and citation. The experimental data consisted of 5,605 articles from the Journal between the years 1889-2007 held in the Journal Storage digital library. The results show that the Latent Dirichlet Allocation model can effectively detect distinct topics and those documents that explained these topics over time. [ABSTRACT FROM AUTHOR]