Keyword Cleaner
Strip punctuation, numbers, extra spaces and stop words from a keyword list.
Options
Results appear here. Everything is processed in your browser — nothing you type is sent anywhere.
What the Keyword Cleaner does
The Keyword Cleaner normalises a messy list in one pass: casing, punctuation, stray numbers, filler words and duplicates are all handled with individual switches you control.
How it works
Each toggle maps to one deterministic transformation applied in a fixed order — punctuation, numbers, stop words, whitespace collapse, lowercasing, then deduplication. The summary shows the real before-and-after counts.
- 1Paste the messy list, one keyword per line.
- 2Turn on only the cleaning rules you want.
- 3Generate and check the summary counts.
- 4Verify nothing important was stripped.
- 5Export the clean list for the rest of your workflow.
Worked example
' Best Running Shoes!!' and 'RUNNING shoes' become 'best running shoes' and 'running shoes'.
Who uses it
- Preparing an exported list from a spreadsheet
- Normalising tags copied from several posts
- Cleaning transcripts before extraction
- Standardising a list before clustering
Best practices
- Clean before you cluster, sort or convert to hashtags.
- Leave number removal off for year- or size-based keywords.
- Keep stop-word removal off for natural long-tail phrases.
- Check the summary — a big drop usually means an over-aggressive rule.
Honest limitations
Cleaning is literal. Removing stop words can break readable long-tail phrases, so review the output rather than trusting it blindly.
Frequently asked questions
- Does cleaning change meaning?
- It can. Stop-word removal in particular shortens natural phrases, so use it deliberately.
- Is the original list kept?
- No, only the cleaned output is returned. Keep your source elsewhere.
- What counts as a duplicate?
- Case-insensitive exact matches after cleaning.