Stop words are very common function words (a, the, and, is, etc.) that carry little lexical meaning on their own. Filtering them out helps surface the meaningful content words in your text.

Is the analysis case-sensitive?

By default, analysis is case-insensitive, so "Apple" and "apple" are counted as the same word. Enable the Case Sensitive toggle to treat them differently.

What text formats are supported?

Plain text is supported. Paste any text - articles, code, speeches, book chapters - and the tool will tokenize and count words. HTML tags are not stripped automatically; paste plain text for best results.

How is percentage calculated?

Each word's percentage is its count divided by the total number of words (after stop-word filtering, if enabled), expressed as a percentage. It shows how much of the text is occupied by that word.

Word Frequency Counter - Analyze Text Word Usage

# ▲▼	Word ▲▼	Count ▼	Frequency ▲▼
1	the	5	13.5%
2	fox	3	8.1%
3	quick	3	8.1%
4	dog	2	5.4%
5	lazy	2	5.4%
6	a	1	2.7%
7	and	1	2.7%
8	at	1	2.7%
9	barked	1	2.7%
10	been	1	2.7%
11	brown	1	2.7%
12	but	1	2.7%
13	dogs	1	2.7%
14	exercises	1	2.7%
15	for	1	2.7%
16	foxes	1	2.7%
17	have	1	2.7%
18	jumps	1	2.7%
19	long	1	2.7%
20	of	1	2.7%
21	over	1	2.7%
22	part	1	2.7%
23	time	1	2.7%
24	too	1	2.7%
25	typing	1	2.7%

# ▲▼

Word ▲▼

Count ▼

Frequency ▲▼

the

13.5%

fox

8.1%

quick

8.1%

dog

5.4%

lazy

5.4%

2.7%

and

2.7%

barked

2.7%

been

2.7%

brown

2.7%

but

2.7%

dogs

2.7%

exercises

2.7%

for

2.7%

foxes

2.7%

have

2.7%

jumps

2.7%

long

2.7%

over

2.7%

part

2.7%

time

2.7%

too

2.7%

typing

2.7%

What is word frequency analysis?

Word frequency analysis counts how many times each word appears in a text, then ranks words from most to least frequent. It is used in linguistics, readability research, SEO content audits, plagiarism detection, and text summarization.

When to use stop-word filtering

Common function words, the, a, is, of, and, account for a large share of any English text but carry little distinctive meaning. Filtering them reveals the keywords that define your content. Leave the filter off when analyzing grammatical patterns or measuring cohesion.

Use cases

SEO: check whether your target keywords appear with the right density.
Academic writing: identify overused words and add variety.
Speeches and scripts: find the ideas you return to most.
Learning languages: discover the most frequent words in a text to study first.

TF-IDF explanation

TF-IDF (Term Frequency–Inverse Document Frequency) extends raw frequency by weighting words based on how rare they are across a collection of documents. A word that appears often in one document but rarely in others gets a high TF-IDF score - making it a strong keyword for that document. Words that appear in every document (like “the”) get a near-zero score. Formula:

TF-IDF(t, d) = TF(t, d) × log(N / DF(t))

Where TF = term frequency in document d, N = total documents, DF = documents containing term t.

Zipf’s law

In natural language, word frequency follows a power law known as Zipf’s law: the most common word appears approximately twice as often as the second most common, three times as often as the third, and so on. This explains why removing stop words has such a dramatic effect on frequency distributions - a handful of words account for a disproportionate share of all tokens.

Export and analysis workflows

Copy the frequency table and paste into a spreadsheet to create a bar chart of top words.

Python one-liner:

from collections import Counter
Counter(text.lower().split()).most_common(20)

For large corpora, import into NLTK or spaCy for stop word filtering and lemmatisation before counting.