How to Remove Punctuation from Text Online (Commas, Periods, All Symbols)

Punctuation is great. It tells you where sentences end, separates items in lists, and keeps meaning clear. Until you need to get rid of it.

Maybe you’re preparing text for a machine learning model. Maybe you need a clean word list without all the commas and periods cluttering things up. Maybe you’re doing frequency analysis and “hello” and “hello!” shouldn’t be counted as different words.

Whatever the reason, removing punctuation sounds simple but has a bunch of gotchas that’ll trip you up if you’re not careful.

When You’d Actually Want to Do This

Before getting into the how, let’s talk about the why. Because “remove all punctuation” is almost never what you actually want. Usually you want to remove most punctuation while keeping certain characters that matter.

NLP and text analysis. This is the big one. When feeding text into natural language processing models, punctuation usually gets stripped during preprocessing. Word embeddings, sentiment analysis, topic modeling, and keyword extraction all typically work on tokenized text without punctuation. Though it’s worth noting that modern transformer models (like BERT, GPT) often handle punctuation just fine and sometimes perform better with it left in.

Word frequency counts. If you’re counting how many times each word appears, punctuation creates false distinctions. “dog” and “dog,” and “dog.” are all the same word, but a simple word counter treats them as different because the punctuation is attached. Strip the punctuation first, then count.

Data normalization. You’re importing data from multiple sources and need consistent formatting. Stripping punctuation from names, codes, or identifiers can help standardize entries before deduplication.

Search index building. Search engines strip punctuation internally when building their index. If you’re building a simple search feature, preprocessing text to remove punctuation before indexing improves match quality.

Comparing texts. When you’re checking if two strings are “essentially the same” ignoring formatting, removing punctuation (along with lowercasing) is a standard normalization step.

The Easy Way: Use a Tool

TextSorter’s Clean Text tool handles punctuation removal along with its other cleanup functions. Paste your text, get clean output. Browser-based, private, no account needed.

For more targeted removal (like stripping specific characters only), the Find and Replace tool with regex support gives you precise control.

The Regex Approach (For Exact Control)

Regular expressions are the programmer’s way to strip punctuation. Here are the most common patterns:

Remove everything that’s not a letter, digit, or space

[^A-Za-z0-9\s]

Replace all matches with nothing (empty string). This leaves only English letters, numbers, and whitespace.

Input: Hello, world! How are you? I'm fine (thanks). Output: Hello world How are you Im fine thanks

Notice the problem? “I’m” became “Im” and “thanks” lost its parentheses. The apostrophe in contractions gets removed because it’s punctuation.

Remove only specific punctuation marks

If you want to keep apostrophes in contractions:

[.,;:!?(){}\[\]"\/\\@#$%^&*~`<>|+=_]

This explicitly lists the characters to remove and leaves apostrophes and hyphens alone. More work to set up, but preserves “don’t” as “don’t” and “well-known” as “well-known.”

Unicode-aware punctuation removal

For text in non-English languages, the ASCII-only regex fails because it also strips letters like “é,” “ñ,” and “ü.” Use the Unicode punctuation category instead:

\p{P}

This matches any character classified as punctuation in Unicode, regardless of language. In JavaScript (with the /u flag):

text.replace(/\p{P}/gu, '')

In Python:

import regex  # pip install regex (not the built-in re)
cleaned = regex.sub(r'\p{P}', '', text)

Language-Specific Code Examples

JavaScript

// Remove all non-alphanumeric (ASCII only)
const clean = text.replace(/[^a-zA-Z0-9\s]/g, '');

// Remove Unicode punctuation (keeps accented letters)
const clean = text.replace(/\p{P}/gu, '');

// Remove specific characters only
const clean = text.replace(/[.,!?;:'"()\[\]{}]/g, '');

Python

import string

# Using string.punctuation (ASCII only)
clean = text.translate(str.maketrans('', '', string.punctuation))

# string.punctuation contains: !"#$%&'()*+,-./:;<=>?@[\]^_`{|}~

The str.translate approach is very fast in Python because it operates at the C level rather than iterating character by character.

Excel / Google Sheets

Excel doesn’t have a built-in “remove punctuation” function, but you can stack SUBSTITUTE calls:

=SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(SUBSTITUTE(A1,".",""),",",""),"!",""),"?","")

Yeah, this gets ugly fast. For serious text cleaning in a spreadsheet, paste the column into the Clean Text tool, process it there, and paste the results back.

In Google Sheets, you can use REGEXREPLACE:

=REGEXREPLACE(A1, "[[:punct:]]", "")

The [[:punct:]] POSIX class matches standard punctuation characters. Much cleaner than stacking SUBSTITUTE calls.

The Contraction Problem

This deserves its own section because it catches people all the time.

When you remove apostrophes, contractions break:

For casual text cleaning, “dont” and “cant” are fine. Your brain still reads them correctly.

For NLP preprocessing, this can actually cause problems. “It’s” (contraction of “it is”) and “its” (possessive pronoun) are different words with different meanings. If you strip the apostrophe, they become the same token and you lose semantic information.

Some NLP preprocessing pipelines handle this by expanding contractions before removing punctuation: “don’t” becomes “do not,” “can’t” becomes “cannot,” “I’m” becomes “I am.” Then you can safely strip all remaining punctuation without losing meaning.

Punctuation Characters: The Full List

Here’s a comprehensive reference of what’s typically considered punctuation in English:

Standard sentence punctuation: . (period), , (comma), ; (semicolon), : (colon), ! (exclamation), ? (question mark)

Quotation marks: ” ” (double quotes), ’ ’ (single quotes/apostrophe), ” ” (smart/curly double quotes), ’ ’ (smart/curly single quotes)

Brackets and enclosures: ( ) (parentheses), [ ] (square brackets), { } (curly braces), < > (angle brackets)

Dashes and hyphens: - (hyphen), – (en dash), — (em dash)

Other standard: … (ellipsis), / (slash), \ (backslash), & (ampersand), @ (at sign)

Extended symbols often treated as punctuation: # (hash), $ (dollar), % (percent), ^ (caret), * (asterisk), _ (underscore), ~ (tilde), ` (backtick), | (pipe), + (plus), = (equals)

Non-English punctuation: ¿ ¡ (Spanish), « » (French/European quotation marks), 。、（）【】 (CJK punctuation), ، ؛ (Arabic punctuation)

The Unicode standard defines over 700 characters in the “Punctuation” category. ASCII-only regex patterns miss all the non-English ones.

Common Mistakes When Removing Punctuation

Mistake 1: Destroying URLs and email addresses. URLs rely on punctuation (dots, slashes, colons, @ signs). If your text contains URLs and you strip all punctuation, https://example.com becomes httpsexamplecom. Either extract URLs first, or selectively skip patterns that look like URLs.

Mistake 2: Breaking decimal numbers. The number 3.14 becomes 314 if you strip periods. If your text contains numbers with decimal points, you need a smarter regex that keeps periods between digits.

Mistake 3: Fusing words together. “Hello!World” becomes “HelloWorld” if you just delete the punctuation without replacing it with a space. Better to replace punctuation with a space first, then collapse multiple spaces.

Mistake 4: Removing hyphens from compound words. “well-known” becomes “wellknown” and “self-employed” becomes “selfemployed.” If hyphens in compound words matter for your use case, keep them.

Mistake 5: Not handling smart quotes. Word processors and some websites use curly/smart quotes (” ” ’ ’) instead of straight quotes (” ’). Your regex might catch straight quotes but miss the curly ones, leaving weird characters in your cleaned text. TextSorter’s Clean Text tool handles smart quote normalization automatically.

Mistake 6: Removing punctuation from the original data. Always work on a copy. If you strip punctuation from your only copy of a document, you can’t get it back. Punctuation carries meaning, and you might need the original.

The NLP Perspective: Should You Even Remove Punctuation?

There’s actually a debate in the NLP community about this. Older text processing pipelines (bag-of-words, TF-IDF) almost always removed punctuation because it was noise.

Modern transformer models (BERT, GPT, and their descendants) were trained on text with punctuation. They understand that a question mark changes the meaning of a sentence and that commas affect clause boundaries. For these models, keeping punctuation often improves performance.

The current consensus is roughly: remove punctuation for simple statistical analysis (word counts, keyword extraction, similarity scores). Keep it for deep learning models and tasks where meaning matters (sentiment analysis, question answering, text generation).

Best Practice Workflow

1. Make a copy of your original text
2. Normalize quotes first (convert smart quotes to straight quotes)
3. Expand contractions if they matter for your analysis
4. Remove punctuation using the appropriate method
5. Replace punctuation with spaces (not empty strings) to avoid word fusion
6. Collapse multiple spaces into single spaces
7. Trim leading and trailing whitespace

The Clean Text tool does steps 2, 5, 6, and 7 automatically. For the full workflow, combine it with Find and Replace for step 4.

Clean your text now →