How to Export YouTube Comments to Excel or Google Sheets (Step-by-Step)

TL;DR: Scrape the comments with TubeHarvest and export as Excel or CSV, open the file in Excel or Google Sheets, then sort by likes, filter to top-level comments, and run a pivot table to find patterns. No API key and no code required. The steps below cover both Excel and Sheets end to end.

Getting comments into a spreadsheet is where most tutorials stop. The useful part is what you do once they are there. This guide does both: the export, then the cleanup, sorting, and pivoting that turn 4,000 rows into something you can act on.

Step 1: Export the Comments as a Spreadsheet File

You cannot copy comments out of YouTube directly, so use a scraper.

1. Open TubeHarvest’s comment downloader.
2. Paste the video URL and click Scrape Comments.
3. When the preview appears, click Download and choose Excel (.xlsx) for Excel, or CSV for Google Sheets.

For thousands of comments or a whole channel, use the Cloud version, which runs server-side and drops finished files in the Exports tab. Full method list: how to download YouTube comments.

Step 2: Open the File

Excel: double-click the .xlsx, or open a CSV with Data → From Text/CSV so the columns split cleanly.

Google Sheets: go to File → Import → Upload, select the CSV, and choose “Replace current sheet.” Sheets detects the comma delimiter automatically.

You should see one row per comment with columns for comment text, author, publish time, likes, replies, and comment type.

Step 3: Clean It Up

A two-minute setup that makes everything after it easier:

• Freeze the header row (Excel: View → Freeze Panes → Freeze Top Row; Sheets: View → Freeze → 1 row).
• Turn on filters (Excel: Data → Filter; Sheets: Data → Create a filter).
• Widen the comment column and enable text wrap so you can actually read it.
• Format publish time as a date if it imported as text.

Step 4: Sort and Filter for Signal

This is where the data starts talking.

• Sort by likes, descending. The most-liked comments are the ones the audience agrees with. Read the top 50 first.
• Filter the comment type to top-level only. Replies are conversational back-and-forth and dilute the signal when you are mining for ideas.
• Filter by keyword. Use the column filter to show only rows containing “how,” “but,” or “wish” to isolate questions and objections fast.

Step 5: Find Patterns With a Pivot Table

To go from reading to measuring, pivot the data.

Excel: select the range, Insert → PivotTable. Drop Author name into Rows and Comment ID into Values (set to Count) to see who comments most. Add Likes to Values (Sum) to find your highest-impact commenters.

Google Sheets: Insert → Pivot table, then the same row and value setup.

A pivot grouping by author surfaces your superfans and your loudest critics. Grouping by date (by month) shows how comment volume tracks with what you published.

Bonus: Two Google Sheets Formulas

Count how many comments mention a topic:

=COUNTIF(A2:A4000, "*pricing*")

Pull every comment that contains a keyword into its own list:

=FILTER(A2:A4000, ISNUMBER(SEARCH("refund", A2:A4000)))

Swap the keyword to scan for objections, feature requests, or competitor names in seconds.

What to Do With the Insights

A clean, sorted sheet is the input, not the output. Once it is set up, run the five comment analysis workflows: content gaps, an objection bank, a competitor teardown, sentiment tracking, and a voice-of-customer swipe file.

Comments to Spreadsheet

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