Only matching values are returned, making the result easy to copy or review.
Thanks @alex and @design.team for the review.
[
"@alex",
"@design.team"
]
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Text
Ready
Mention Extractor
Extract unique @mentions from authorized text and preserve the order in which accounts first appear.
Predictable processing Source remains editable Separate result Copy-ready output
INPUT Source text to scan
Paste authorized text that contains the values this extractor should collect. Example: Thanks @alex and @design.team for the review.
Reading file…
Extracted mentions
Matching values appear as a copy-ready list.
Practical examples
See how this tool works
Each example shows a realistic source value and the result produced by this specific tool.
Only matching values are returned, making the result easy to copy or review.
Assigned to @maria and reviewed by @qa.team.
[
"@maria",
"@qa.team."
]
About the tool
What Mention Extractor does
Mention Extractor is a focused browser tool that helps you extract unique @mentions from authorized text and preserve the order in which accounts first appear.
The workspace keeps the source available for comparison, applies a defined rule, and presents the result separately. This makes the change easier to inspect before content is published, imported, shared, or used in another workflow.
Use the complete source whenever possible, verify context-sensitive values, and retain a backup for important documents or structured data.
How to use
- Paste. Add the complete source text.
- Configure. Enter any values required by the tool.
- Run. Apply the named operation.
- Review. Compare the source and result carefully.
- Copy. Use the output only after verification.
Built for the task
Why use Mention Extractor?
Focused controls, predictable output, and a workflow designed around this exact transformation.
01
Predictable processing
A defined rule makes the result easier to understand.
02
Source remains editable
The original text stays available for comparison.
03
Separate result
The generated output is displayed independently.
04
Copy-ready workflow
Review first, then copy the approved result.
Useful answers
Questions about Mention Extractor
Practical details about input, output, privacy, limits, and the best way to use this tool.
Answer 01
What counts as a mention?
A mention begins with @ and continues through supported letters, numbers, dots, hyphens, or underscores.
Answer 02
Are repeated mentions removed?
Yes. Unique values are returned in first-seen order.
Answer 03
Can it verify an account exists?
No. It only extracts matching text and does not query a platform.
Answer 04
Can email addresses create false matches?
Yes. The part after @ in an email may resemble a mention, so review context.
Answer 05
Does it crawl social media?
No. It processes only the text you provide.
Answer 06
Are private mentions safe to share?
Review authorization and privacy before exporting or redistributing account references.
Answer 07
Does the result include @?
Yes. Extracted values include the leading at sign.
Answer 08
Is the source changed?
No. The source remains separate from the extracted list.
What counts as a mention?
A mention begins with @ and continues through supported letters, numbers, dots, hyphens, or underscores.
Are repeated mentions removed?
Yes. Unique values are returned in first-seen order.
Can it verify an account exists?
No. It only extracts matching text and does not query a platform.
Can email addresses create false matches?
Yes. The part after @ in an email may resemble a mention, so review context.
Does it crawl social media?
No. It processes only the text you provide.
Are private mentions safe to share?
Review authorization and privacy before exporting or redistributing account references.
Does the result include @?
Yes. Extracted values include the leading at sign.
Is the source changed?
No. The source remains separate from the extracted list.
Mention Extractor — How Mention Extractor finds account references
Mention Extractor scans the supplied text for tokens beginning with the @ symbol and returns matching values as a clean list. It is useful for reviewing approved captions, support transcripts, community notes, event summaries, and campaign drafts without copying the surrounding prose.
Review the source and generated result together, keep an untouched backup, and confirm the output in the destination system before relying on it.
Mention Extractor — Unique mentions and original order
Repeated account references are removed from the result while the first occurrence remains. Preserving first-seen order makes it easier to compare the list with the source and can reflect the sequence in which participants were introduced.
Review the source and generated result together, keep an untouched backup, and confirm the output in the destination system before relying on it.
Mention Extractor — Mentions versus email addresses
An email address contains an @ symbol, so part of an email can resemble a social mention. Extraction is pattern-based rather than context-aware. Inspect every result and use the dedicated email extractor when the source primarily contains contact addresses.
Review the source and generated result together, keep an untouched backup, and confirm the output in the destination system before relying on it.
Mention Extractor — Platform naming rules
Different platforms allow different characters, lengths, and capitalization rules in usernames. A token that looks valid in text may not be an active account on the intended service. Verify names directly on the relevant platform before publishing or tagging.
Review the source and generated result together, keep an untouched backup, and confirm the output in the destination system before relying on it.
Mention Extractor — Privacy and authorized use
Account references can be personal data or part of a private conversation. Process only content you are allowed to use, avoid unnecessary redistribution, and review whether each mention is appropriate for the final audience.
Review the source and generated result together, keep an untouched backup, and confirm the output in the destination system before relying on it.
Mention Extractor — Combining mention analysis with other tools
Use hashtag extraction for campaign tags, email extraction for addresses, and text statistics for the original draft. Separate tools reduce ambiguity and make the source-to-result relationship easier to audit.
Review the source and generated result together, keep an untouched backup, and confirm the output in the destination system before relying on it.
Related Trexmi tools
Continue with a related converter, a cleanup tool, or a text analysis tool. Each operation remains separate so changes are easier to understand and reverse.
External reference
For general string-processing concepts, consult the MDN String reference.