AudienceCue

Shorts comments are notoriously low-signal — emoji spam and inside jokes drown out real feedback. Does the report separate substantive feedback from noise, or weight comments by engagement when building the citations? For research use cases, filtering noise matters more than comment volume, so I'd love to know how you handle that.

This sounds good. I will give it a try. I have made a game names wordable can any one let me knoe how is it. https://wordable.live/

Hi! I built AudienceCue after watching creators read thousands of YouTube comments by hand. Paste a public video, Short, channel, or playlist link and AudienceCue saves the comments YouTube returns, exports them as CSV/JSON/TXT/XLSX, and turns them into a cited AI report. It never posts, moderates, or takes actions on your channel. Feedback very welcome!

I must try this out for myself

Keeping every finding tied to source comments is the part that makes this useful rather than another summary that invents a vibe. Exporting the raw comments as CSV or JSON alongside the report also means a researcher can check the sample, not just the narrative. One thing I would look for is how the report marks comments YouTube truncated or failed to return,

The cited-to-source framing is what separates this from a generic AI summary. Does a citation survive if the commenter later edits or deletes it, or does the report just keep the snapshot as-downloaded?

Turning public comments into cited reports could be very useful for validating audience needs without losing the source context. The export options also make the workflow flexible for research and marketing teams.

Love this approach to YouTube analytics. The ability to export raw comment data for deeper analysis is critical - most tools give you summaries, but researchers need the full dataset. This solves a real gap in the YouTube creator toolkit.

Smart idea! Turning comments into reports saves hours. Does it support other languages? Great work!

This tool is incredibly useful for content creators and marketers! It allows you to download public YouTube comments and turn them into highly detailed, AI-driven reports. I love that it supports multiple export formats like CSV, JSON, and XLSX, and that every finding in the report links back to the original source evidence. It saves a massive amount of time and is perfect for audience analysis. Highly recommended!"

The cited-report workflow is a strong differentiator—keeping findings tied to source comments should make research easier to audit. Support for channels and playlists also seems especially useful for comparing themes across a broader sample.

Cited audience reports are the right idea - the citations are what make YouTube comment analysis defensible in front of clients. How do you filter out spam and bot comments before they skew the report?

Tying every finding to the source comment is what makes this usable for real decisions. One thing that would help video creators a lot: comments often mention timestamps like "at 2:14 you said...". If the report grouped feedback by those timestamps, you'd see exactly which moments of a video people reacted to, not just the overall sentiment. Is that on the roadmap?

Keeping AI findings tied to the original public comments is a meaningful trust advantage over generic sentiment summaries. The support for playlists and URL lists should help researchers move from one-off examples to a broader, citable audience dataset.

Tying every finding to a real comment is what makes an AI summary usable, because you can check it. Public sample reports before anyone spends a credit is a good call too. Is there a cap on how many comments you can pull for a large channel?

Very useful tool. Love it

The citation-first reporting is a strong differentiator—keeping findings tied to source comments should make audience research easier to audit and share with a team.

Thats an awesome idea, i'll give it a go!

Cited audience reports from public YouTube comments could save creators and researchers a lot of manual sorting, especially with exports to CSV, JSON, TXT, or XLSX. I like the read-only design and the fact that it keeps findings tied to source evidence rather than posting or moderating on a channel. Can you combine multiple videos or playlists into one comparative report?

The evidence-linked reports and export formats seem especially useful for turning noisy YouTube feedback into research you can actually cite. I also like that it keeps the workflow focused on public data without posting or moderating on a channel.

The cited-findings approach is the right call - most AI sentiment reports are unverifiable black boxes, so tying every finding back to its source comment solves the trust problem outright. The free no-signup downloader is also a smart funnel. Two questions: how do the AI reports handle non-English comments, and in channel-level reports can you separate comments by video so feedback across a series can be compared? Also curious whether creator replies are threaded into the analysis or counted as separate rows.

Keeping every finding tied to source comments is the part that makes this useful rather than another summary that invents a vibe. Exporting the raw comments as CSV or JSON alongside the report also means a researcher can check the sample, not just the narrative. One thing I would look for is how the report marks comments YouTube truncated or failed to return, so a cited claim is not mistaken for a complete census of the thread.

AudienceCue looks like a useful tool for anyone trying to better understand and reach their target audience. I like the focus on making audience research more practical and actionable for creators and businesses. https://wordlehint.tips/