An empirical comparison of four text mining methods

Sangno Lee; Jeff Baker; Jaeki Song; James C. Wetherbe

doi:10.1109/HICSS.2010.48

An empirical comparison of four text mining methods

Sangno Lee, Jeff Baker, Jaeki Song, James C. Wetherbe

Business Administration

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

35 Scopus citations

Abstract

The amount of textual data that is available for researchers and businesses to analyze is increasing at a dramatic rate. This reality has led IS researchers to investigate various text mining techniques. This essay examines four text mining methods that are frequently used in order to identify their advantages and limitations. The four methods that we examine are (1) latent semantic analysis, (2) probabilistic latent semantic analysis, (3) latent Dirichlet allocation, and (4) the correlated topic model. We compare these four methods and highlight the optimal conditions under which to apply the various methods. Our paper sheds light on the theory that underlies text mining methods and provides guidance for researchers who seek to apply these methods.

Original language	English
Title of host publication	Proceedings of the 43rd Annual Hawaii International Conference on System Sciences, HICSS-43
Publisher	IEEE Computer Society
ISBN (Print)	9780769538693
DOIs	https://doi.org/10.1109/HICSS.2010.48
State	Published - 2010
Event	43rd Annual Hawaii International Conference on System Sciences, HICSS-43 - Koloa, Kauai, HI, United States Duration: Jan 5 2010 → Jan 8 2010

Publication series

Name	Proceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)	1530-1605

Conference

Conference	43rd Annual Hawaii International Conference on System Sciences, HICSS-43
Country	United States
City	Koloa, Kauai, HI
Period	01/5/10 → 01/8/10

Access to Document

10.1109/HICSS.2010.48

Link to publication in Scopus

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abstract = "The amount of textual data that is available for researchers and businesses to analyze is increasing at a dramatic rate. This reality has led IS researchers to investigate various text mining techniques. This essay examines four text mining methods that are frequently used in order to identify their advantages and limitations. The four methods that we examine are (1) latent semantic analysis, (2) probabilistic latent semantic analysis, (3) latent Dirichlet allocation, and (4) the correlated topic model. We compare these four methods and highlight the optimal conditions under which to apply the various methods. Our paper sheds light on the theory that underlies text mining methods and provides guidance for researchers who seek to apply these methods.",

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Lee, S, Baker, J, Song, J & Wetherbe, JC 2010, An empirical comparison of four text mining methods. in Proceedings of the 43rd Annual Hawaii International Conference on System Sciences, HICSS-43., 5428645, Proceedings of the Annual Hawaii International Conference on System Sciences, IEEE Computer Society, 43rd Annual Hawaii International Conference on System Sciences, HICSS-43, Koloa, Kauai, HI, United States, 01/5/10. https://doi.org/10.1109/HICSS.2010.48

An empirical comparison of four text mining methods. / Lee, Sangno; Baker, Jeff; Song, Jaeki; Wetherbe, James C.

Proceedings of the 43rd Annual Hawaii International Conference on System Sciences, HICSS-43. IEEE Computer Society, 2010. 5428645 (Proceedings of the Annual Hawaii International Conference on System Sciences).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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