In this guide, we introduce researchers in the behavioral sciences in general and MIS in particular to text analysis as done with latent semantic analysis (LSA). The guide contains hands-on annotated code samples in R that walk the reader through a typical process of acquiring relevant texts, creating a semantic space out of them, and then projecting words, phrase, or documents onto that semantic space to calculate their lexical similarities. R is an open source, popular programming language with extensive statistical libraries. We introduce LSA as a concept, discuss the process of preparing the data, and note its potential and limitations. We demonstrate this process through a sequence of annotated code examples: we start with a study of online reviews that extracts lexical insight about trust. That R code applies singular value decomposition (SVD). The guide next demonstrates a realistically large data analysis of Stack Exchange, a popular Q&A site for programmers. That R code applies an alternative sparse SVD method. All the code and data are available on github.com.
Gefen, David; Endicott, James E.; Fresneda, Jorge E.; Miller, Jacob; and Larsen, Kai R.
"A Guide to Text Analysis with Latent Semantic Analysis in R with Annotated Code: Studying Online Reviews and the Stack Exchange Community,"
Communications of the Association for Information Systems: Vol. 41
, Article 21.
Available at: https://aisel.aisnet.org/cais/vol41/iss1/21