Principle:DistrictDataLabs Yellowbrick Text Feature Visualization

Knowledge Sources	Text Analysis with Yellowbrick
Domains	NLP, Visualization
Last Updated	2026-02-08 05:00 GMT

Overview

Techniques for visually analyzing the statistical and structural properties of text corpora, including word frequency, co-occurrence, dispersion, POS distribution, and document similarity.

Description

Text feature visualization encompasses multiple complementary views of a corpus: frequency distributions reveal vocabulary concentration, word correlation heatmaps show co-occurrence patterns, dispersion plots reveal term distribution across documents, POS tag frequencies characterize grammatical structure, and t-SNE projections reveal document similarity clusters. Together these views support feature engineering and model interpretation for NLP tasks.

Usage

Use this principle during exploratory analysis of text data, before or after feature extraction, to understand corpus characteristics and validate vectorization choices.

Theoretical Basis

Term Frequency: Count of term occurrences across the corpus.

Word Correlation: Pearson correlation between term occurrence vectors across documents.

$r_{i j} = \frac{\sum_{d} (c_{i d} - {\bar{c}}_{i}) (c_{j d} - {\bar{c}}_{j})}{\sqrt{\sum_{d} (c_{i d} - {\bar{c}}_{i})^{2} \cdot \sum_{d} (c_{j d} - {\bar{c}}_{j})^{2}}}$

Lexical Dispersion: Positional distribution of terms within a concatenated corpus.

t-SNE Projection: Nonlinear dimensionality reduction of document vectors to 2D for cluster visualization.

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Principle

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