Text mining with correspondence analysis

Here is an example of the use of correspondence analysis for textual data. Four methods of multivariate data analysis are descibed by words and compared with...

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Exploratory multivariate analysis with R and FactoMineR

Data analysis methods summary

Principal component analysis (part1: data - practicalities)

Principal component analysis (part 2/3: studying individuals and variables)

Principal component analysis (part 3/3: interpretation aids)

Principal component analysis (PCA) with R

PCA with FactoMineR

Factoshiny: interactive graphs in exploratory multivariate data analysis

Handling missing values in PCA

Correspondence Analysis (part 1/5): Introduction

Correspondence Analysis (part 2/5): Visualizing the row and column clouds

Correspondence Analysis (part 3/5): Inertia and percentage of inertia

Correspondence Analysis (part 4/5): Simultaneous representation

Correspondence Analysis (part 5/5): Interpretation aids

Correspondence Analysis with FactoMineR

Text mining with correspondence analysis

Mutltiple Correspondence Analysis (Part 1/4: Data - issues)

Multiple Correspondence Analysis (part 2/4: Visualizing the point cloud of individuals)

Multiple Correspondence Analysis (Part 3/4: Visualizing the cloud of categories)

Multiple Correspondence Analysis (Part 4/4: Interpretation aids)

Multiple Correspondence Analysis with FactoMineR

Multiple correspondence analysis on an incomplete dataset

Handling missing values in MCA

Clustering (part 1/4: introduction)

Clustering (part 2/4: example and how to choose the number of clusters)

Clustering (part 3/4: the partitioning method K-means)

Clustering (part 4/4: characterizing clusters)

Clustering with FactoMineR

Factorial Analysis on Mixed Data

Tools to interpret the results obtained by principal component methods (PCA, MCA, MFA)

Multiple Factor Analysis (MFA) with R using FactoMineR

Text mining with correspondence analysis

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Exploratory multivariate analysis with R and FactoMineR

Data analysis methods summary

Principal component analysis (part1: data - practicalities)

Principal component analysis (part 2/3: studying individuals and variables)

Principal component analysis (part 3/3: interpretation aids)

Principal component analysis (PCA) with R

PCA with FactoMineR

Factoshiny: interactive graphs in exploratory multivariate data analysis

Handling missing values in PCA

Correspondence Analysis (part 1/5): Introduction

Correspondence Analysis (part 2/5): Visualizing the row and column clouds

Correspondence Analysis (part 3/5): Inertia and percentage of inertia

Correspondence Analysis (part 4/5): Simultaneous representation

Correspondence Analysis (part 5/5): Interpretation aids

Correspondence Analysis with FactoMineR

Text mining with correspondence analysis

Mutltiple Correspondence Analysis (Part 1/4: Data - issues)

Multiple Correspondence Analysis (part 2/4: Visualizing the point cloud of individuals)

Multiple Correspondence Analysis (Part 3/4: Visualizing the cloud of categories)

Multiple Correspondence Analysis (Part 4/4: Interpretation aids)

Multiple Correspondence Analysis with FactoMineR

Multiple correspondence analysis on an incomplete dataset

Handling missing values in MCA

Clustering (part 1/4: introduction)

Clustering (part 2/4: example and how to choose the number of clusters)

Clustering (part 3/4: the partitioning method K-means)

Clustering (part 4/4: characterizing clusters)

Clustering with FactoMineR

Factorial Analysis on Mixed Data

Tools to interpret the results obtained by principal component methods (PCA, MCA, MFA)

Multiple Factor Analysis (MFA) with R using FactoMineR

Text mining with correspondence analysis

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