Work

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

Year: 2019

Type: article

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.

Source: Journal of Ubiquitous Systems and Pervasive Networks

DOI: 10.5383/juspn.11.01.002

Authors: Trefor Williams, John Betak

Institutions: Rutgers, The State University of New Jersey, The University of Texas at Austin

Language: English

Topic: Computational and Text Analysis Methods

Subfield: General Social Sciences

Field: Social Sciences

Domain: Social Sciences

Open Access status: diamond

OpenAlex ID: W2944933014

FWCI: 2.941

Cites: 9

Cited by: 11

Related to: 10