About AI Text Classifier & Topic Categorizer
Client-side probabilistic Naive Bayes text classifier. Analyzes word frequency distributions to categorize unstructured documents into Technology, Business & Finance, Science & Medicine, Entertainment, Politics, and Sports.
Key Capabilities & Features
- Multi-domain Naive Bayes classification model with Laplace smoothing
- Categorizes across 6 core domains (Technology, Business, Science, Media, Politics, Sports)
- Visual progress meters showing percentage probabilities for each domain
- Lists specific keyword tokens that triggered each category score
- Handles multi-paragraph articles and short snippets smoothly
How to Use AI Text Classifier & Topic Categorizer
1
Input Text
Paste an article, blog post, or document snippet.
2
Inspect Probabilities
Review the primary predicted category and domain percentage breakdown.
3
Review Keywords
Examine the matched terms that contributed to the classification.
Privacy & In-Browser Execution Guarantee
100% Private. Text tokenization and probability calculations run locally on your device.
Frequently Asked Questions
How does the classifier determine the topic?
It tokenizes the input, filters out common stop words, and computes Bayesian domain probabilities against weighted vocabulary profiles.
Can it classify mixed-topic articles?
Yes! The probability breakdown shows the exact distribution across all 6 domains.
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