Bypassing the YouTube Data API’s pagination overhead and strict quota consumption rules is the real unlock here. Writing custom Python scrapers or dealing with nested comment thread architectures just to pull a clean dataset is a tedious time sink. This utility abstracts that entire data extraction pipeline—cleanly flattening top-level comments, nested replies, author handles, like counts, and precise video timestamps into structured CSV or JSON formats without forcing you to configure local proxies or manage authorization tokens. Having the data immediately structured into a uniform schema drastically accelerates the initial phase of audience sentiment mining, customer research, or sourcing product feedback.
@kapil_dev_pal We've mined comment sections by hand for positioning language before and it's useful but painfully slow. Does the analysis surface themes and sentiment, or is the export the product and the analysis is on me? Would want to know if it can pull comments across a competitor's whole channel, not just one video.
Congrats on the launch! Genuinely curious about the technical side: are you pulling comments through the official YouTube Data API or scraping them? I'm asking because the API has pretty strict daily quotas, and scraping tends to break whenever YouTube changes their frontend. How are you handling rate limits for videos with tens of thousands of comments? Would love to hear how you've thought about the reliability side, that seems like the make-or-break factor for a tool like this.
@dennis_weijer1
Thank you! 😊 Great question—reliability was one of our biggest considerations when building the tool.
We currently use the official YouTube Data API, so we're not relying on frontend scraping. That gives us a much more stable and reliable foundation while staying aligned with YouTube's platform.
For larger videos, the tool automatically handles pagination to fetch comments in batches and exports them as the data is retrieved. The practical limit is determined by the available YouTube API quota rather than our application itself.
As the product grows, we're planning to add smarter features like background jobs, resumable exports, progress tracking, and caching for large exports to make the experience even smoother.
Really appreciate the thoughtful question—it's exactly the kind of engineering challenge we're continuing to optimize. 🚀
How does it handle videos with thousands of comments, does it cap the export or pull everything in one go? Also wondering if the filtering works before or after the data is fetched.
@rukiyexemn Great question! Currently, the exporter is designed to fetch all available comments, including large comment sections, using pagination rather than trying to load everything in a single request. This keeps the process reliable even for videos with thousands of comments. Filters are applied during the export process wherever possible to reduce unnecessary data, and you can further refine the results before downloading.
skipping the API-vs-scraping question since Dennis already asked it and that's the one I'd want answered first too. separate question: does the export preserve reply threading (which comments are replies to which), or does it flatten everything into one list? that matters a lot for the "product ideas and customer questions" use case, a reply only makes sense in context of what it's replying to, and flattened it just reads as a random opinion.
@galdayan
That's a great question, and it's something we designed for from the start. 😊
The export preserves the reply structure—it doesn't just flatten everything into a single list. Each reply includes its parent comment reference, so you can easily reconstruct the conversation and understand the context behind every reply.
In CSV/Excel, we include fields like Is Reply, Parent Comment ID, and Reply Count, while the JSON export maintains the conversation hierarchy even more naturally. We agree that context is essential when analyzing product ideas, customer questions, or feedback, so preserving those relationships was a priority.
We're also working on visual conversation threads and AI-powered discussion summaries to make these conversations even easier to explore. Thanks for bringing this up! 🚀
How does it handle videos with thousands of comments — is there a hard cap per video, or can it pull the full thread including replies?
@sezerdasekwcwb Great question! It can fetch the complete comment thread, including replies, for public YouTube videos. There isn't a fixed limit imposed by our tool—the number of comments retrieved mainly depends on the YouTube Data API quotas and the video's available comments. For videos with thousands of comments, the tool automatically paginates through all available results and lets you export them to CSV, Excel, or JSON. We're also working on adding progress indicators and resumable exports for very large datasets.
Would love to see a sentiment analysis column added to the export, so I can quickly sort comments by positive, negative, or neutral without having to run them through another tool afterwards.
@ancacypedd50847 Thanks for the suggestion, Anurag! That's a great idea and definitely something I've been considering. An AI-powered sentiment analysis column (Positive, Neutral, and Negative) would make it much easier to analyze audience feedback without any extra steps. I'll add it to the roadmap—thanks for the valuable feedback! 🚀
Love how the export formats and filtering options are all laid out without burying them in menus. Most scraping tools make you dig through settings just to get the basics, this one just works right out of the gate.
@grkemhasanlmce Thank you, That was exactly the experience I wanted to create. I wanted the tool to be simple enough that you can paste a YouTube URL, apply filters if needed, and export your data without navigating through complicated settings. Really appreciate you noticing the focus on usability—it means a lot! 🙌