Hashtag & Mention Extractor — Pull #tags and @handles
Extract hashtags (#tag) and @mentions from social posts, captions, articles. Frequency count. Free, in-browser.
About Hashtag & Mention Extractor
A hashtag and mention extractor scans text — social-media post drafts, scraped feeds, blog content — and pulls every hashtag (#topic) and @mention (@handle), deduplicating, lowercasing, and counting frequency for content audits, hashtag strategy planning, or competitive social analysis. The ZTools Hashtag & Mention Extractor runs entirely in the browser, handles Twitter/X, Instagram, LinkedIn, TikTok conventions (Unicode hashtags supported), and exports lists with frequency counts as plain text or CSV.
Use cases
- Social-media content audit. Paste 50 of your past posts; extractor outputs every hashtag used, ranked by frequency. Identify under- or over-used tags; align with content strategy.
- Competitor hashtag research. Scrape a competitor's recent posts; extract their hashtag set. Compare with yours to find gaps or trending tags worth adopting.
- Influencer mention map. Identify which accounts are mentioned across a campaign or topic. Useful for collaboration outreach or sentiment analysis prep.
- Trend discovery. Paste a Twitter/X feed dump; extract all hashtags and mentions to surface most-discussed topics in real time.
How it works
- Paste source text. Social posts, captions, comments, blog text. Mixed formats handled.
- Apply pattern matching. Hashtag: # followed by 1+ letters/digits/underscores (Unicode aware). Mention: @ followed by handle characters.
- Validate. Drops bare # and bare @, common false positives (HTML entities, code fragments).
- Aggregate frequency. Counts how often each tag/mention appears. Lowercase normalisation merges #SEO and #seo.
- Export. Sorted list (alphabetical or frequency), CSV with tag + count, or plain text.
Examples
Input: "Loved the #seo session by @smithy at #marketingweek2024"
Output: Hashtags: #seo, #marketingweek2024. Mentions: @smithy.
Input: 50 captions, mixed
Output: Top hashtags: #seo (12), #content (9), #marketing (7), #ai (5).
Input: Unicode: "#한국 #日本 #🎉"
Output: Unicode hashtags preserved; emoji hashtag included if option enabled.
Frequently asked questions
How are case differences handled?
Default: case-insensitive — #SEO and #seo merge. Optional case-sensitive mode for platforms where case matters (rare on social media).
What about emoji hashtags?
Toggleable. Modern Unicode regex supports emoji codepoints. Some platforms (LinkedIn) strip them; Twitter/X and Instagram preserve.
Are mentions resolved to real accounts?
No — extraction is text-only. To verify accounts, run the list through each platform's API or manual lookup.
Will it extract from screenshots?
Run the screenshot through OCR (image-to-text-ocr) first, then the extracted text through this tool.
Is the input uploaded?
No — client-side only.
How do I find trending tags I should use?
Extract from your audience's posts (not yours). Their tags reveal what they search; you match those.
Pro tips
- Combine your historical post extraction with your audience's tags — overlap reveals the strategically valuable tags.
- Don't over-tag. Twitter/X: 1-3 tags optimal. Instagram: up to 30 allowed but 5-10 outperforms 30 in tests.
- Track tag frequency over time — declining usage signals a tag is losing relevance.
- Mentions in comments often indicate community; mentions in posts often indicate collaboration. Different signals.
- Filter by frequency to find the long-tail of niche tags worth experimenting with.
Reviewed by Ahsan Mahmood · Last updated 2026-05-05 · Part of ZTools.
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