Applying Correspondence Analysis for NLP in Base R

 

Example: Applying Correspondence Analysis for NLP in Base R

Dataset: Term-Document Matrix

We have a hypothetical term-document matrix representing word frequencies across three documents:

	Doc1	Doc2	Doc3
Apple	3	0	2
Banana	1	4	0
Cherry	0	5	1
Date	2	0	3
Elderberry	0	1	4

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Step-by-Step Code in R

1. Create the Term-Document Matrix

R
# Create a term-document matrix
terms <- c("Apple", "Banana", "Cherry", "Date", "Elderberry")
Doc1 <- c(3, 1, 0, 2, 0)
Doc2 <- c(0, 4, 5, 0, 1)
Doc3 <- c(2, 0, 1, 3, 4)

# Combine into a matrix
term_doc_matrix <- matrix(c(Doc1, Doc2, Doc3), nrow = 5, byrow = FALSE)
rownames(term_doc_matrix) <- terms
colnames(term_doc_matrix) <- c("Doc1", "Doc2", "Doc3")

# View the matrix
print(term_doc_matrix)

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2. Install and Load the Required Package

Base R does not directly support CA, so we use the ca package. If not installed, install it using:

R
install.packages("ca")

Load the package:

R
library(ca)

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3. Apply Correspondence Analysis

R
# Perform Correspondence Analysis
ca_result <- ca(term_doc_matrix)

# Print the CA summary
summary(ca_result)

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4. Visualize the Results

R
# Plot the Correspondence Analysis results
plot(ca_result, main = "Correspondence Analysis: Term-Document Matrix")

This plot shows the reduced 2D space where:

• Terms (words) and documents are plotted.
• Words close to a document are more associated with it.

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5. Interpret the Results

The CA output includes:

1. Row Coordinates (terms): Indicates how words are distributed across documents.
2. Column Coordinates (documents): Indicates the relationship of documents to terms.
3. Eigenvalues: Indicates how much variance each dimension explains.

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Extension: Use CA Results in AI

Extract Coordinates for Machine Learning

The CA results can be used as features in AI models.

R
# Extract row (terms) and column (documents) coordinates
term_coordinates <- ca_result$rowcoord
doc_coordinates <- ca_result$colcoord

# View term coordinates
print(term_coordinates)

# View document coordinates
print(doc_coordinates)

These coordinates represent terms and documents in a reduced-dimensional space (e.g., 2D). They can be fed into clustering or classification models.