Stylometric Analysis PATENT-44
Stylometric Analysis creates a unique "fingerprint" for YouTube creators by analyzing writing style, presentation patterns, and visual composition across their videos. This fingerprint can be used to identify similar creators, detect content attribution, and provide detailed side-by-side comparisons of creator styles.
Step-by-Step Explanation
User Input
The user enters a YouTube video URL to begin the stylometric analysis. The system accepts any valid YouTube video link and begins the feature extraction process.
Extract Transcript and Visual Features
The system retrieves the video transcript (auto-generated or manual captions) and extracts visual features including frame composition, color usage, editing patterns, and on-screen text. These raw features form the basis for fingerprint construction.
Build Stylometric Fingerprint
Three feature vector categories are computed in parallel. Writing Style Vectors capture vocabulary richness, sentence structure, and rhetorical patterns from the transcript. Presentation Patterns analyze pacing, tonal shifts, and transition styles. Visual Composition Metrics measure color palettes, shot composition, and editing cadence.
Compare Against Database
The generated fingerprint is compared against TubeRaker's database of previously analyzed creators using cosine similarity and other distance metrics. The system identifies creators whose stylometric signatures are most similar.
Match Results
Results are displayed as a ranked list of similar creators with similarity percentages. Each match shows which specific dimensions (writing, presentation, or visual) contribute most to the similarity score.
Fingerprint Detail Page
Users can drill into the full fingerprint detail view, which shows the complete vector breakdown across all analyzed dimensions with interactive visualizations of each feature category.
Side-by-Side Comparison Tool
The comparison tool allows users to place two creators side-by-side, highlighting differences and similarities across all stylometric dimensions. This is useful for content attribution analysis and understanding creator influence patterns.