Comment Analyzer

The comments under a video are the most direct feedback a creator gets, but reading hundreds of them by hand is slow and the loudest ones stick. Paste a public post link and Kabo reads up to about 150 of the most-liked comments (about 60 of the latest on Instagram), then groups them into the patterns that matter for your next upload.

• Sentiment split An estimate of how positive, neutral or negative the sample is, so you can tell a heated thread from a happy one at a glance.
• Recurring themes with real quotes The topics that keep coming up, each with the share of comments and one quote copied from an actual comment. If no matching comment exists, the quote is dropped instead of invented.
• Questions, requests and complaints What viewers are asking, what they want you to make next, and what bothered them.
• Video ideas grounded in the comments Concrete follow-up ideas that answer what your audience already asked for.

Answer the questions in public.

A question that shows up again and again is a ready-made title. Answer it in a follow-up video and pin a reply that links to it.

Look at the requests list before you plan your next week. If several viewers ask for a part 2, a tutorial or a product link, that demand is already proven. Complaints are worth a look too: repeated notes about audio, pacing or a missing detail are cheap to fix.

Use a public YouTube video or Short, a TikTok video, or an Instagram post or Reel. Comments must be public and enabled. The analysis uses comment text and like counts only — it does not show or store who wrote each comment.

• It is a sample. Very large threads are summarized from their most-liked top-level comments; replies are not included.
• Sentiment is an estimate. Sarcasm and in-jokes can be misread, so check the quotes before acting on a theme.
• Competitor research works too. Run it on a popular video in your niche to see what that audience still wants answered.