Remove Prefix from Words
Strip text, patterns, or characters from the beginning of every word.
Configuration
Clean the Clutter: Strip Prefixes from Every Word
When processing social media posts, tagged datasets, or versioned code, word prefixes create noise. Hashtags (#) and mentions (@) that were useful for discovery now clutter plain text analysis. Variable names with test_, old_, or temp_ prefixes need cleaning after refactoring. List numbers (1., 2., 3.) prepended to items become redundant when importing to databases. Manually removing these from hundreds of words is tedious—but automated prefix stripping is instant.
The Remove Prefix from Words tool automates word-beginning cleanup. It offers three powerful removal modes: remove specific strings (like "#" or "@"), use regex patterns for advanced matching (like [#@]+ to strip multiple symbols), or simply delete the first N characters from every word. The tool also includes unwrapping to remove brackets/quotes first, word filtering to skip short words, and trim options to clean leading whitespace—all while processing thousands of words per second in your browser.
Why Remove Prefixes from Words?
- ✓Social Media Cleanup: Strip hashtags (#) and mentions (@) from posts for text analysis, sentiment scoring, or database imports without tags.
- ✓Code Refactoring: Remove version prefixes (test_, old_, v1_) from variable names after code cleanup or database schema migrations.
- ✓Data Preparation: Clean list markers (1., A., -, >), category tags, or identifier prefixes from word lists for CSV imports or analytics processing.
- ✓Text Normalization: Strip formatting symbols, currency indicators, or special markers from words to create clean, standardized text datasets.
Common Use Cases
Social Media Analysis
Remove hashtags (#) and mentions (@) from Twitter, Instagram, or LinkedIn posts before sentiment analysis, keyword extraction, or topic modeling. Essential for NLP pipelines that need clean text without social media formatting.
Post-Refactoring Cleanup
Strip temporary prefixes (test_, old_, backup_) from variable, function, and class names after code refactoring. Clean database column names that were marked with version indicators during schema migrations.
List Processing
Remove numbered prefixes (1., 2., A., B.) from list items before importing to databases or spreadsheets. Clean bullet markers (-, >, *) from markdown-formatted lists for plain text processing.
SEO Data Cleaning
Strip platform-specific tags and markers from keyword lists exported from social media analytics, SEO tools, or content management systems before analysis or reporting.
See it in action
Professional Features
Precise Text Removal
Target exact strings to remove. Only words starting with your text will be modified.
Regex Support
Advanced users can use Regular Expressions to match complex patterns like list numbers or multiple symbols.
Count Removal
Don't know the exact text? Simply delete the first N characters from every word.
Smart Unwrapping
Remove surrounding brackets `[word]` or quotes `"word"` first, then apply your main prefix removal.
Word Filtering
Skip short words (articles, prepositions) by setting minimum word length. Protect small words from modification.
Private & Secure
0% Server usage. The tool runs entirely in your browser memory for maximum data privacy.
How to use this tool
Input Content
Paste your text with prefixed words or upload a file.
Choose Mode
Select "Specific Text", "Regex Pattern", or "First N Chars".
Set Target
Enter the text, pattern, or number of characters to remove.
Copy Result
Copy the clean text to clipboard or download as a file.