Automatically conducts a Principal Components Analysis (PCA) on a text variable using Google's Universal Encoder to convert the text variable to numeric data. The results are presented in a modified loadings table that shows the cross-correlation between the principal component scores and an augmented document term matrix for ease of interpretation.
The example below shows the input and Principal Components Analysis (Text) output for a survey question where respondents were asked about their opinion of Tom Cruise. In particular, what they didn't like about Tom Cruise. The responses are shown in this table.
The example below conducts a Principal Components Analysis (Text) with four components and uses a varimax type rotation to aid interpretation. A snapshot of how the object inspector looks for this output is here:
The output displays a modified loadings table that shows the correlation of different words and phrases with the numeric variables that describe the text. If the table entries are sorted by size, as is in the table output below, each column has its entries sorted such that they are in decreasing order of the magnitude of the correlations. These correlations are between the component scores and the word and phrase variables. The words and phrases with the largest correlations with component one are shown first, and then followed by the largest correlations with component two and so on. Scroll down the output table to see the largest correlations with components three and four.
Options
Text variable The text variable containing responses that you would like to analyze in the Principal Component Space.
Truncate cases when characters exceed Truncate cases whose number of characters exceeds this number. This is done as the algorithm expects text data to consist of one sentence per case and may not function correctly when cases are too long. If any cases are truncated due to this setting (a warning will be shown), it is likely that the data does not conform to this assumption (one sentence per case) and is not appropriate for this analysis.
Rule for selecting components Method for determining the number of principal components to keep in the analysis:
Number of components Manually select the number of components to keep.
Eigenvalue over Keep components with eigenvalues greater than a user-specified number.
Rotation method (see below for more details):
Varimax A Varimax type rotation of the principal components is used by default to produce solutions where the cross-correlation of the principal component scores and an augmented documentation term matrix has entries closer to 0, 1, or -1, making interpretation of the solution easier.
None The original principal component scores are used in creating the cross-correlation matrix with an augmented document term matrix for each case.
Sort coefficients by size When displaying loadings or the structure matrix sort the components according to their size.
Suppress small loadings When displaying loadings or the structure matrix, replace small values with blank spaces to facilitate interpretation.
Absolute value below In tables, cells which have absolute values smaller than this will be replaced with blank spaces.
Rotations
A Varimax type rotation of the principal components is used by default to produce solutions where the cross-correlation of the principal component scores and a type of documentation term matrix has entries closer to 0, 1, or -1, making interpretation of the solution easier.
The Varimax-type rotation is orthogonal, meaning that the components produced are always uncorrelated with one another.
Scores
Principal components analysis can be used to create a new set of variables which give the new values for each case on the components that have been identified. The initial analysis is done without rotation and computed using the Regression method. If a Varimax type rotation is specified (default), then the principal component scores are rotated with an orthogonal matrix to maximize the variance of the cross-correlation matrix of the rotated principal components scores against the presented document term type matrix. The scores can be saved using the Dimension Reduction - Save Variable(s) feature that is also available as an action button in the Inputs tab.
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How to Create Principal Components Analysis for a Text Variable in Displayr