How to Use a Word Frequency Map to Analyze Text

What the word frequency map does

A word frequency map helps you see which retained terms appear most often in a passage, draft, transcript, or other supplied text. It can filter stop words, ignore tokens below a chosen minimum length, and limit how many terms appear in the displayed list. This makes it useful for a quick vocabulary review or for spotting repeated wording before you revise a document.

The analysis works from the text you provide. Since filtering changes which tokens remain, read the results as a summary of the retained terms rather than a count of every word in the original passage.

How to analyze text

1. Prepare the text. Copy the passage, notes, transcript, or draft you want to review. Text may be omitted, but an omitted value produces an empty result rather than a word list.

2. Provide the text. Paste the content into the text input. The analysis lowercases the text. Characters other than ASCII letters, whitespace, and apostrophes are replaced with spaces, and the remaining content is split at whitespace. For example, numbers, most punctuation, and non-ASCII letters do not remain in the analyzed tokens.

3. Choose stop-word filtering. Stop-word filtering starts enabled. When it is enabled, terms in the built-in stop-word set are excluded after minimum-length filtering. Turn it off when those terms are relevant to the question you are investigating.

4. Set the minimum token length if needed. The default is 3 characters. A supplied value is converted to an integer, with a falsy value using 3, and the resulting setting cannot be lower than 1. Use a higher threshold to focus on longer terms; use 1 when short terms matter.

5. Set the displayed-term limit. The default is 30 terms. A supplied limit is converted to an integer, with a falsy value using 30, and the normalized value is constrained from 5 through 100. Choose a larger limit when you need a broader list.

6. Run the analysis and review the list. Retained terms are ordered from the highest count to the lowest count. Each displayed term includes its count, a percentage based on its count among retained terms, and a relative bar value. The percentage is rounded to two decimal places, while the bar value is rounded to one decimal place.

How to read the results

Start with the total retained-token count and the number of distinct retained terms. The total counts tokens that remain after normalization and filtering. The distinct-term figure shows how many different retained terms were found. A term near the top has a higher retained count than terms below it, while its percentage describes its share of the retained-token total.

The displayed-term limit affects what appears in the list, not the underlying distinction between the retained-token total and distinct retained terms. If the limit is lower than the retained vocabulary, some distinct terms will not be displayed. Stop-word filtering and the minimum-length setting can also make the retained totals smaller than the word count you would get by reading the original text without filters.

For a useful comparison, keep the text and settings consistent between runs. If a result looks unexpected, check whether punctuation, digits, accented or other non-ASCII letters, short tokens, or built-in stop words changed what was retained.

Worked example

You are reviewing the sentence "Clear writing makes clear instructions" and want to see which retained term occurs most often.

Paste the sentence into the text input, leave the default filters enabled, keep the default minimum length and displayed-term limit, and run the analysis.

The result contains a ranked list of retained terms with each term's count, a percentage rounded to two decimal places, and a relative bar value rounded to one decimal place. It also reports the total retained-token count and the number of distinct retained terms. With the default filters, "clear" is the most frequent retained term, appearing 2 times out of 5 retained tokens.

Limitations

• Results cover retained tokens after normalization, stop-word filtering, and minimum-length filtering; the displayed list may omit retained distinct terms when its limit is lower than the retained vocabulary.

Common errors

• A frequent mistake is passing a non-text value or a truthy minimum-length or displayed-term setting that cannot be converted to an integer. Provide text as text, and use numeric settings that convert cleanly; otherwise processing may fail instead of returning a handled result.

FAQ

Can I run the analysis without entering text?

Yes, but an omitted or empty text value succeeds with no word entries and zero totals. Provide text when you want a frequency list.

What are the default analysis settings?

The default minimum token length is 3 characters, and the default displayed-term limit is 30. Both settings can be adjusted within their normalization rules.

How are the frequency results ordered and calculated?

The list is ordered by descending count and is limited by the normalized displayed-term setting. Percentages use the retained-token total and are rounded to two decimal places.