I've been toying with the idea of building a YouTube comment analysis tool—something inspired by GummySearch but focused entirely on YouTube as a discovery platform. The goal? To extract valuable insights from video comments and turn them into actionable ideas. Since I'm building this in public, I want to share my thought process, challenges, and what I've learned so far.

The Core Idea

The tool I'm working on will allow users to input YouTube channels or specific videos manually. Once added, the tool will analyze all the comments associated with those videos, surfacing trends, opinions, and discussions. Essentially, it's about making sense of the massive amount of user-generated content on YouTube.

Structuring the Interface

One of the biggest questions I had to answer early on was: How should the interface be structured to make the experience intuitive?

Here’s the layout I’ve been experimenting with:

• Left Panel: This is where users can add sources—either entire YouTube channels or specific videos they want to analyze.
• Center Panel: This is where all the extracted comments will be displayed, along with insights generated from the data.
• Top Buttons (Above the Center Panel): These will allow users to filter and analyze comments in different ways, such as:
  • Generating product ideas
  • Extracting content suggestions
  • Running sentiment analysis
  • Categorizing comments into positive and negative feedback
• Right Panel: This section serves as an interactive conversation panel where users can explore insights in a more structured way, either through a chat-like interface or a summary table.

This is a rough sketch of the prototype:

What the Tool Will Help With

After refining the concept, I realized that this tool could be incredibly valuable for:
✅ Identifying trends and product opportunities by analyzing what people are talking about.
✅ Understanding audience sentiment, both overall and by specific themes.
✅ Collecting content ideas based on what viewers are already discussing.
✅ Aggregating and summarizing feedback across multiple sources in one place.

By structuring the tool this way, it becomes much easier to spot recurring themes in comments, whether they’re complaints, feature requests, or content suggestions.

Exporting Insights

One feature I knew I needed was the ability to export insights. Once users analyze a set of comments, they should be able to export the results as a PDF—this makes it easy to share findings with a team or save them for later reference.

Challenges & Next Steps

Of course, building this tool comes with challenges. One of the biggest hurdles is ensuring that the comment analysis is efficient and meaningful—it’s not just about collecting comments, but actually extracting valuable insights. Some key challenges I’ll need to solve:

• Efficiently scraping and processing large volumes of YouTube comments.
• Implementing accurate sentiment analysis that doesn’t misinterpret sarcasm or nuanced opinions.
• Designing a UI that makes navigating insights intuitive and actionable.

Next, I’ll be working on refining the sentiment analysis and making sure the tool can handle a variety of content types. Ultimately, the goal is to provide a comprehensive and data-driven view of YouTube discussions that helps creators, marketers, and product builders make better decisions.

Learning in Public

Since I’m building this tool from scratch, I’ll be documenting my progress here. If you’re interested in data analysis, YouTube research, or building similar tools, let’s connect! I’d love to hear your thoughts, feedback, or any suggestions you might have.

Would you use a tool like this? What features would be most valuable to you?