Text Classifier
Classify text into categories using keyword-based scoring with preset classifiers for spam, sentiment, topic, support tickets, and content type. Custom labels supported — fully client-side.
How to Use
Classify text into categories in three steps:
- Select a classifier — Choose a built-in preset (Spam Detection, Sentiment, Topic, Support Ticket, Content Type) or click "Custom" to define your own categories with comma-separated keywords.
- Paste your text — Enter any text to classify. Click a sample button to try pre-loaded examples. Toggle between single-label mode (one winner) and multi-label mode (independent scoring).
- Review results — See the top category with confidence bars for all categories. Matched keywords are shown beneath each bar. Copy the classification summary with the clipboard button.
About This Tool
Keyword-Based Classification
The classifier scores text against each category by counting keyword matches. Multi-word phrases (like "machine learning" or "click here") receive higher weight than single words, since multi-word matches are more indicative of the target category. Scores are normalized by text length to ensure fair comparison regardless of document size.
In single-label mode, raw scores are passed through a softmax function that produces a probability distribution summing to 100%. This is appropriate when categories are mutually exclusive (e.g., an email is either spam or not). In multi-label mode, each category's score is independently transformed via sigmoid, allowing text to belong to multiple categories simultaneously — for example, a support ticket about a billing bug would score high on both "Billing" and "Technical."
Built-in Presets
Five curated presets cover common classification tasks. Spam Detection uses 50+ spam, promotional, and business keywords. Sentiment covers positive, negative, and neutral emotional vocabulary. Topic classifies into technology, finance, health, sports, and politics. Support Ticket routes into billing, technical, feature request, and account categories. Content Type distinguishes news, opinion, academic, and tutorial writing styles.
Accuracy and Limitations
Keyword-based classification is transparent and predictable — the matched terms are always visible, so you can understand exactly why a classification was made. However, it cannot capture context, synonyms, or implied meaning. A sentence like "this product is not bad" would match "bad" in the negative category despite being positive. For context-aware classification, zero-shot NLI models are needed. For sentiment analysis with negation handling, see Sentiment Analyzer.
Why Use This Tool
Instant Browser-Side Classification
All classification runs in your browser with zero latency. Keyword lists total approximately 2KB. Processing is instantaneous for text of any length. No API keys, no rate limits, no ML model downloads. Custom categories take effect immediately.
Common Use Cases
- Email triage: Quickly classify incoming emails as spam, promotional, or legitimate business correspondence. Useful for testing email filtering rules.
- Support ticket routing: Determine whether a customer message is about billing, technical issues, feature requests, or account management.
- Content moderation: Pre-screen user-generated content by topic or content type before human review.
- Research coding: Apply consistent category labels to survey responses, interview transcripts, or review text for qualitative research.
- SEO content audit: Verify that page content matches its intended topic category by checking keyword density per category.
Privacy
100% client-side. Your text never leaves your browser. Related tools: Sentiment Analyzer, Keyword Extractor, Text Similarity Checker, and Readability Analyzer.