Export Word Frequency to CSV — Analyse Text Data in Excel

Updated: May 2026

The browser-based frequency counter is convenient for quick checks. For deeper analysis — comparing multiple documents, building charts, filtering by thresholds, or sharing results with a team — exporting to CSV unlocks the full power of spreadsheet software.

Export word frequency to CSV →

Free · No upload · Instant download

What the CSV export contains

The exported file follows a simple four-column structure:

rank,word,count,density
1,"content",47,2.35%
2,"text",38,1.90%
3,"frequency",31,1.55%
4,"analysis",28,1.40%
5,"words",24,1.20%

Each row represents one unique word from your text. The columns are: rank (position by frequency, 1 = most frequent), word (the exact token as it appeared or lowercased if you chose case-insensitive mode), count (number of occurrences), and density (count divided by total word count, as a percentage).

The file is UTF-8 encoded, which means it correctly handles accented characters, dashes, apostrophes and other non-ASCII content that appears in English text. Open it in Excel, Google Sheets, LibreOffice Calc, or any programming language with a CSV parser.

Opening the CSV in Excel

If you double-click the CSV file and Excel opens it, the columns may not split correctly if your system uses a semicolon delimiter rather than a comma. The safe method:

Open Excel. Go to Data → From Text/CSV.
Browse to the downloaded file and click Import.
In the preview, set the delimiter to Comma and the file origin to UTF-8.
Click Load. The data appears in a properly formatted table.

In Google Sheets, simply drag the file onto the browser window or use File → Import. Google Sheets handles UTF-8 CSV correctly by default without additional configuration.

Once the data is in a spreadsheet, convert it to a Table (Ctrl+T in Excel, Format as Table in Google Sheets). This enables instant sorting, filtering and charting without manual range selection.

Useful analyses to run in Excel after the export

The CSV is a starting point. Here are the most valuable next steps in a spreadsheet:

Density threshold filter: filter the density column to show only words above 1% to identify potential over-optimization candidates, or below 0.5% to find words that appear too rarely to be meaningful.
Frequency histogram: select the count column and insert a bar chart. This visualizes the distribution — how steeply it drops from the most frequent word tells you how diverse the vocabulary is.
VLOOKUP comparison: export two documents and use VLOOKUP to match words across both. Add a third column showing the difference in density — positive values mean your document emphasizes this word more, negative values mean the competitor does.
Top N summary: use a SUMIF to calculate the combined density of the top 10 words. A healthy content article typically has its top 10 content words accounting for 15–25% of total density. Much higher suggests narrow vocabulary; much lower suggests broad but potentially unfocused content.
Conditional formatting: apply a red-yellow-green color scale to the density column. This turns the table into a visual heat map — glance at it and the high-density words are immediately obvious without sorting.

Multi-document comparison workflow

The most powerful use of CSV export is comparing word distributions across multiple texts. This is especially useful for competitive SEO analysis and for tracking how vocabulary evolves across versions of a document.

Step-by-step for a competitor comparison:

Copy the full visible text from competitor page 1. Paste into the frequency counter. Export as CSV. Rename the file to "competitor-1.csv".
Repeat for competitor pages 2 and 3, and for your own page.
In a new spreadsheet, create one sheet per file. In a fifth "comparison" sheet, list all unique words from all four files in column A.
Use VLOOKUP in columns B–E to pull each document's density for each word.
Sort by the average of columns B–D (competitors) descending. Words with high competitor density and low density in column E are your semantic gaps.

This workflow is more accurate than relying on SEO tools that scrape and process text server-side, because you control exactly what text is included — you can strip navigation, footers, and sidebar content that those tools sometimes include.

Using the JSON export for programmatic analysis

Developers who want to process frequency data programmatically may prefer the JSON export over CSV. The JSON format includes rank, word, count, and density for each entry, structured as an array of objects — directly parseable by JavaScript, Python, or any language with a JSON library.

[
  {"rank":1,"word":"content","count":47,"density":2.3500},
  {"rank":2,"word":"text","count":38,"density":1.9000},
  {"rank":3,"word":"frequency","count":31,"density":1.5500}
]

This format is useful for feeding frequency data into a custom NLP pipeline, for comparing results programmatically across many documents, or for building dashboards that aggregate frequency analysis across a content library.

Privacy note: export is local

Both the CSV and JSON exports are generated entirely in your browser. The Blob URL created for the download file never touches a server. Your text remains private throughout the analysis and export process. The exported file lives only on your device from the moment you click the download button.

This matters when working with sensitive content — legal documents, confidential business plans, unpublished manuscripts, or personal correspondence. You can safely analyse and export frequency data from any document without the risk of it being processed, stored, or indexed by a third-party service.