Sciweavers

Share
HICSS
2010
IEEE

An Empirical Comparison of Four Text Mining Methods

12 years 1 months ago
An Empirical Comparison of Four Text Mining Methods
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 characteristics and limitations. The four methods that we examine are (1) latent semantic analysis, (2) probabilistic latent semantic analysis, (3) latent Dirichlet allocation, and (4) correlated topic model. We review these four methods and compare them with topic detection and spam filtering to reveal their peculiarity. Our paper sheds light on the theory that underlies text mining methods and provides guidance for researchers who seek to apply these methods.
Sangno Lee, Jeff Baker, Jaeki Song, James C. Wethe
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where HICSS
Authors Sangno Lee, Jeff Baker, Jaeki Song, James C. Wetherbe
Comments (0)
books