The Latent Semantic Analysis option produces two SVD Plots. The first plot shows the first two singular vectors for the documents, that is, the first two columns of the U matrix. This plot is equivalent to the Score Plot in the Principal Components platform. Each point in this plot represents a document (row of the data table). You can select the points in this plot to select the corresponding rows in the data table.

The second plot shows the first two singular vectors for the terms, that is the first two rows of the V‘ matrix. This plot is equivalent to the Loadings Plot in the Principal Components platform. In this plot, the points correspond to rows in the Term List table.

Below the document and term SVD plots, a table of the singular values appears. These are the diagonal entries of the S matrix in the singular value decomposition of the document term matrix. The Singular Values table also contains a column of corresponding eigenvalues for the equivalent principal components analysis. Like in the Principal Components platform, there are columns for the percent and cumulative percent of variation explained by each eigenvalue (or singular value). You can use the Cum Percent column to decide what percent of variance from the DTM you want to preserve, and then use the corresponding number of singular vectors.