How to analyze YouTube comments

Reading a thousand comments one scroll at a time is not analysis, and YouTube's own comment panel shows a sorted slice with no way to export the rest.

Sorting by top comments surfaces a handful of popular replies and hides everything else, including comments that are more critical or more specific. There is also no way to save the results for later comparison once a video's comment section keeps growing.

Sentiment Analysis collects every comment and reply on a video, or on up to ten videos at once, along with likes, reply counts, authors, timestamps, and whether a comment is pinned, edited or hearted by the creator.

This includes replies nested under a top-level comment, not just the first layer, so a conversation thread does not get cut off partway through. Fields like reply counts and whether a comment was pinned or hearted also carry a signal about which comments the creator engaged with.

As an optional second step, AI scoring rates each comment for sentiment, emotion, toxicity and language, which turns a wall of text into something that can be sorted, filtered and counted.

Language detection also matters for a video with an international audience, where comments in several languages would otherwise need separate manual review. Together, these dimensions make it possible to ask a specific question, such as how sentiment differs between languages, rather than reading everything at once.

The result exports as CSV, Excel or JSON, ready for a spreadsheet or a notebook. Collection is priced per hundred comments, and AI scoring separately per hundred, both shown before the job runs.

A CSV or XLSX export drops straight into a spreadsheet for filtering and pivot tables, while JSON suits a script or notebook that will process the data further. Because collection and scoring are metered separately, a batch of ten videos can be collected first and scored selectively afterward.