Extract Hashtags from Text

Our parser uses modern Unicode property escapes (\p{L}\p{N}_) that support all international scripts natively. Accented Latin characters (#café, #español, #München), Cyrillic (#Москва), Greek (#Ελλάδα), Arabic (#دبي), Japanese Kana (#あさごはん), and Chinese Hanzi (#科技) are extracted completely without premature truncation or corrupted symbols.

In natural writing and social captions, hashtags are often followed immediately by punctuation, such as '#webdev, #reactjs!' or enclosed in parentheses '(#travel)'. Our smart boundary trimming detects and removes sentence-boundary punctuation (. , ! ? : ; ) ] } > " ') so your extracted tags remain pristine and ready for use.

Yes! Simply uncheck the 'Include "#" Symbol' toggle in the configuration toolbar. The tool will immediately strip leading hash symbols, producing bare keywords. When combined with the Comma delimiter, you get a clean list (e.g. 'travel, photography, bali') ready to paste directly into YouTube tags or CMS keyword fields.

When 'Deduplicate (Unique)' is enabled, tags differing only by letter case (such as #React, #react, and #REACT) are grouped into a single canonical entry, retaining the first-encountered CamelCase format. Under 'By Frequency' sorting, hashtags that appeared most frequently rank at the very top, helping you identify the most prominent campaign tags.

Yes. You can download your formatted hashtag list as a clean .txt file or export an RFC 4180 compliant 2-column .csv spreadsheet ('Hashtag,Frequency'). You can also generate and copy a comprehensive plaintext audit receipt with full metric summaries.

100% private. All text scanning, regex parsing, metrics counting, and file downloads execute strictly within your local browser volatile memory. Zero text drafts, private client messages, or extracted tags are ever uploaded to remote servers or third-party services.