Skip to main content

hashtag-mention-extractor

A hashtag and mention extractor scans text β€” social-media post drafts, scraped feeds, blog content β€” 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​

  1. Paste source text β€” Social posts, captions, comments, blog text. Mixed formats handled.
  2. Apply pattern matching β€” Hashtag: # followed by 1+ letters/digits/underscores (Unicode aware). Mention: @ followed by handle characters.
  3. Validate β€” Drops bare # and bare @, common false positives (HTML entities, code fragments).
  4. Aggregate frequency β€” Counts how often each tag/mention appears. Lowercase normalisation merges #SEO and #seo.
  5. Export β€” 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: "#ν•œκ΅­ #ζ—₯本 #πŸŽ‰"

Output: Unicode hashtags preserved; emoji hashtag included if option enabled.

Frequently asked questions​

How are case differences handled?

Default: case-insensitive β€” #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 β€” 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 β€” 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.

Tips​

  • Combine your historical post extraction with your audience's tags β€” 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 β€” 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.

Try it now​

The full hashtag-mention-extractor runs in your browser at https://ztools.zaions.com/hashtag-mention-extractor β€” no signup, no upload, no data leaves your device.

Open the tool β†—

Last updated: 2026-05-05 Β· Author: Ahsan Mahmood Β· Edit this page on GitHub