A Comparison of LSA and LDA for the Analysis of Railroad Accident Text

Trefor P. Williams · John F. Betak

11 citations0/0 files storedJournal of Ubiquitous Systems and Pervasive Networks

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Computational and Text Analysis Methods Topic Modeling Text and Document Classification Technologies

Abstract

Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation(LDA) were used to identify themes in a database of text about railroad equipment accidents maintained by the Federal Railroad Administration in the United States.These text mining techniques use different mechanisms to identify topics.LDA and LSA identified switching accidents, hump yard accidents and grade crossing accidents as major accident type topics.LSA identified accidents with track maintenance equipment as a topic.Both text mining models identified accidents with tractor-trailer highway trucks as a particular problem at grade crossings.It was found that the use of the two techniques was complementary, with more accident topics identified than with the use of a single method.

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