Email Extractor — Pull Contacts, Links and IPs from Any Text
Email and Contact Extractor
Email addresses — 2
| Emails | 2 |
| URLs | 2 |
| Phone numbers | 2 |
| IP addresses | 1 |
| Social handles | 1 |
| Total unique items | 8 |
Extraction runs entirely in your browser, so pasted text never leaves the page. Numbers inside emails and URLs are excluded from the phone list, and duplicates are collapsed. Pattern matching cannot read context: a nine-digit order number looks exactly like a phone number, so review the output before relying on it.
What Is
A regular expression is a small pattern language for describing text shapes. It can say "a word, then an at sign, then a domain with a dot in it" or "four groups of one to three digits separated by dots", and it can do that across a megabyte in milliseconds. What it cannot do is understand meaning. A regex has no idea whether 4155550132 is a phone number or an order number, whether example.com is reachable, or whether a person consented to be contacted. It matches shapes, and everything hard about extraction lives in that gap between shape and meaning. That gap is widest for email addresses. RFC 5322 defines an address as a local part, an at sign, and a domain, and it permits quoted local parts, comments in parentheses, IP-literal domains such as user@[192.168.0.1], and any character inside a quoted string. Strictly validating an address means implementing a large slice of that grammar. Practical tools therefore compromise: they match a conservative subset and accept that some valid addresses are missed. The pattern used here anchors the match on the character in front of the local part and requires at least one dot-separated label in the domain, which is what catches the overwhelming majority of real addresses while rejecting things like a bare @username handle or the @ symbol in a changelog.
The classic failure of a naive extractor is trailing punctuation. A regex written as "characters, at sign, characters, dot, characters" applied to "Mail us at [email protected]." happily returns [email protected]. because the final dot is a legal character in the grammar. Every scraper hits this, and the result is an inbox full of addresses ending in a full stop, a comma or a closing parenthesis. The fix is a trailing boundary assertion that refuses to extend a match into a following word character or dot, plus a cleanup pass for URL matches that must keep legitimate trailing characters like a closing parenthesis but drop sentence punctuation. Phone numbers are harder still, and one step is genuinely counterintuitive: digits found inside an email address or a URL must be masked out before searching for numbers at all. Consider [email protected] or https://example.com/invoice/20240115 — both contain digit runs that pass any plausible phone pattern. The way to avoid them is to blank those regions out with spaces of the same length before the phone pattern runs, which keeps every other character offset intact so matching elsewhere in the text is unaffected. Even then, false positives are unavoidable. A nine digit order number, a customer ID, a timestamp like 20240115120000, a product code such as A12345678901 — all of them have the shape of a phone number and none of them are one.
Regex cannot look at the surrounding words to decide, which is why the output is a starting point for review rather than a finished list. International formats compound the problem: country codes with a plus, national numbers with a leading zero that must be kept, separators that are dots, spaces, hyphens or parentheses, and extensions. The workable compromise is to count digits and keep only matches with seven to fifteen digits, which covers the shortest national numbers and the longest E.164 numbers while discarding stray two-digit numbers and long identifiers. IPv4 addresses are the one category that can be validated properly, because the format is a fixed shape: four decimal octets separated by dots, each between 0 and 255, with no leading zeros in the modern specification. The pattern matches octet by octet rather than as free digits, so 999.1.1.1 and 192.168.01.1 are correctly rejected, and boundary assertions stop it from carving a fragment out of a version string like 1.2.3.4.5 or an IPv6 address. Social handles are simpler still, but they need a negative lookbehind for @ and # so that an email address never yields a handle for its own local part. Because none of this can run on a server without shipping your text to someone else, everything here is plain JavaScript in the page. There is no upload, no queue and no account, and the cost of that choice is that the work is bounded by the size of text a browser can hold comfortably.
How to Use
- Paste or type your text into the large textarea at the top of the card. The card ships with a sample paragraph containing emails, links, phone numbers, an IP address and a handle, so you can see the format before adding your own content.
- Read the five category buttons below the textarea: Emails, URLs, Phone numbers, IP addresses and Social handles. Each one shows a live count in brackets that updates on every keystroke, so the tabs double as a quick health check on the text you pasted.
- Open the Emails tab to list every address found. Duplicates are collapsed, so a value mentioned ten times still appears once, and the heading under the tabs repeats the total for that category.
- Switch to URLs, Phone numbers, IP addresses or Social handles the same way. Each tab shows either the deduplicated list or the message that nothing of that type was found, which is a fast way to confirm a pattern is missing rather than just absent.
- Read the totals table at the bottom of the card. It lists all five categories with their counts and a bold Total unique items row, which is the figure to quote when you report how much was harvested.
- Select a value in the list and copy it directly, or paste the text back into a spreadsheet column for further processing. Since nothing leaves the page, you can safely work with text containing real contact data.
Examples
Input: Reach the team at [email protected] or ping [email protected] for pricing.
Process:
Match the email pattern, require a dotted domain, dedupe repeated values, then reject the trailing full stop with a boundary assertionResult: Emails (2): [email protected], [email protected]
Input: Docs: https://wrightkit.com/guides and http://example.org/docs?page=2 — office line +1 (415) 555-0132.
Process:
URL matches stop before the em dash, the sentence period is trimmed, then digits inside the links are masked before the phone pattern runsResult: URLs (2): https://wrightkit.com/guides, http://example.org/docs?page=2 | Phone numbers (1): +1 (415) 555-0132
Input: Server 192.168.1.10 handles uploads. Order 4155550132 shipped. Follow @wrightkit.
Process:
IPv4 validated octet by octet, the nine digit order number passes the seven-to-fifteen digit filter and is kept, the handle is matched with an @ lookbehindResult: IP addresses (1): 192.168.1.10 | Phone numbers (1): 4155550132 (review — could be an order id) | Social handles (1): @wrightkit
Input: The same reply to [email protected] and [email protected] within 300 words, IP 10.0.0.256 excluded.
Process:
Identical matches collapse into one row, and the out-of-range IPv4 octet fails validation so nothing is listedResult: Emails (1): [email protected] | IP addresses (0): 10.0.0.256 rejected — octet above 255
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Frequently Asked Questions
Will this tool find every email address in my text?
No extractor can promise that, and the honest reason is the shape of the address grammar rather than a bug. RFC 5322 permits quoted local parts, bracketed IP domains and comment syntax, so a pattern that matched everything would also match nonsense. This tool matches the conservative real-world subset, which reliably catches ordinary addresses while deliberately missing exotic ones. If you control the data, exporting it from the source system is more reliable than parsing text.
Why do digits inside emails and URLs not show up as phone numbers?
Because those regions are blanked out with spaces of identical length before the phone pattern runs, so their digits never reach the matcher while every other character keeps its position. Without that step, a [email protected] address or an invoice URL ending in 20240115 would produce phantom phone results, because any run of digits in the right range looks identical to a number. The same masking logic keeps version numbers and reference codes out of the phone list.
Why is my nine digit order number listed as a phone number?
Because pattern matching sees shape, not meaning. A nine digit order number and a nine digit mobile number are the same string to a regex, and deciding between them requires understanding the words around them, which patterns cannot do. The extractor keeps seven to fifteen digits to stay useful across national and international formats, which deliberately includes most order numbers. Scan the phone list with the surrounding context before acting on it, or split the text so that identifiers are separated from contact blocks.
Which phone number formats does the phone tab recognise?
It handles the shapes that appear most often in real text: optional country code with a plus, optional parenthesised area code, and groups of digits separated by spaces, dots or hyphens. Matches are then filtered by digit count, keeping seven to fifteen digits so that both short national numbers and long international ones survive while stray small numbers are dropped. Formats with unusual separators or written-out country names will not match, and neither will numbers split across a line break in the middle of a group.
Does it handle IPv4 addresses and avoid false IPv6 matches?
IPv4 matching is strict, which is possible because the format is fixed: four decimal octets between 0 and 255 separated by dots. Anything above 255 or with too many or too few octets is rejected, so 999.1.1.1 is ignored. Boundary assertions also stop the pattern from biting a fragment out of a longer dotted string such as a version number, which is the usual source of bogus IP results. IPv6 addresses are not matched at all and will appear only in the URL tab when they are part of a link.
Is the text I paste uploaded to a server?
No. The patterns run as JavaScript inside your browser tab, so the text is read from the textarea and never transmitted, logged or stored. That matters most for exactly the input this tool is built for, since pasted text is frequently a support thread or a customer record full of personal data. The practical consequence of running client-side is only that very large inputs are limited by what a browser page can comfortably hold, rather than by a network round trip.
Can I extract social media handles as well?
Yes, the Social handles tab collects values that start with @ followed by a letter and up to thirty alphanumerics or underscores, which covers the naming rules of the major networks. A negative lookbehind keeps @ from the start of an email address from producing a bogus handle for its local part, and duplicates are collapsed just like every other category. Remember that a handle is only a handle: it does not tell you which network the account lives on, and it can be claimed by anyone.