Comment Explorer is a tool that helps YouTube creators analyze the comments on their videos. It uses AI to extract insights from the comments, such as the most common topics, sentiment, and engagement. This information can help creators to better understand their audience and create content that is more likely to resonate with them.

Comment Explorer is easy to use. Simply enter the URL of your YouTube video and the tool will generate a report that includes the following information:

  • Most common topics: Comment Explorer identifies the most common topics discussed in the comments on your video. This information can help you to understand what your audience is most interested in and to create content that addresses those interests.
  • Sentiment: Comment Explorer identifies the overall sentiment of the comments on your video. This information can help you to understand how your audience is reacting to your content and to make adjustments as needed.
  • Engagement: Comment Explorer identifies the most engaging comments on your video. This information can help you to understand which comments are resonating most with your audience and to create content that is more likely to generate engagement.

 

Overall, Comment Explorer is a powerful and easy-to-use tool that can help YouTube creators to better understand their audience and create content that is more likely to resonate with them.

Chris: Hi there, please introduce yourself.

Griffin: Hello, my name is Griffin and I’m a software developer by trade and creator of the Comment Explorer app. In my career I’ve built software for FinTech and PropTech startups, in a research capacity for a university research group, and at a large robotics company in Canada. My most recent undertaking has been Comment Explorer which aims to unlock the rich body of data in the comment sections of Youtube videos.

Chris: What motivated the development of Comment Explorer, and what specific challenges or needs in the field of content creators on YouTube did you aim to address by providing this tool?

Griffin: My motivations for developing Comment Explorer were two-fold: unlock the “opaque” data in Youtube comment section which is often inaccessible due to the sheer volume of text, and help Youtubers better tailor their content to their audience. The current toolset that YouTube provides creators feels incomplete with regards the comment section, especially in light of recent advancements in LLMs. With Comment Explorer, the aim is to fill the gap in Youtube’s own comment analysis tools and by doing so, help Youtubers understand their audience and create better content.

Chris: The volume of comments on YouTube can be overwhelming. How does Comment Explorer make this data accessible and manageable for content creators, and what insights can they gain from analyzing their comment sections?

Griffin: The first thing Comment Explorer does is reduce the complexity of the problem. In videos with large numbers of comments, a good portion of those are spammy and don’t contain a lot of information. Comment Explorer ignores these comments and analyzes “high signal” comments instead. We then break down the information into different analyses: sentiment analysis, emotional analysis, and subject analysis which provide different lenses to view and understand your comment section with. Comment Explorer also allows you to query your comments to perform your own specific analyses or just find an answer you were looking for.

Chris: When choosing a video to analyze, you mentioned that Comment Explorer places no limits on whose comments can be analyzed. How does this recommendation to analyze other YouTubers’ comments benefit creators, and can you provide examples of insights that are common in specific genres of videos?

Griffin: A key feature of Comment Explorer is that you’re not restricted to analyzing only the comments of your own videos. This opens the game up for analyzing the audiences of your competitors, or other Youtubers in your genre. Analyzing other people’s videos can help you understand how your own audience compares to other audiences in terms of engagement, positivity, depth of discussion and so on. For example, in the genre of science and science communication, one can see through querying the comments that in the most popular videos in the genre, commenters rave about how “effective”, “clear”, and “engaging” their video was. This might seem obvious but it provides clear evidence that to make a popular video in this genre, you’re going to need to focus on breaking down complex ideas and presenting them in a clear and perhaps even story-like manner.

Chris: The Audience Engagement Score is an interesting metric. How is it calculated, and what factors are taken into account when determining the level of audience engagement based on comments, views, likes, and sentiment?

Griffin: Without getting into the nitty-gritty of it, the Audience Engagement Score takes into account the comments, likes and sentiment of the video and most importantly adjusts them based on the view count of the video and what is considered a normal response for a given number of views. This last part is important since videos of different view counts can expect different levels of proportional engagement.

Chris: Emotion analysis using emojis is a unique feature. Can you elaborate on how Comment Explorer identifies and interprets the top three emotions expressed in the comment section, and how this information can be useful to content creators?

Griffin: Comment Explorer uses a neural net trained on large amounts of social media data to determine the amount of a given emotion present in the comment section. The emojis are really just a cute way of showing what emotions from a list of 7 (joy, anger, fear, sadness, surprise, disgust, neutral) are expressed. There are many times such emotional information can be important for creators. It can help creators judge whether a video had the desired impact or can help market researchers and marketers understand what sort of reaction videos are getting and whether or not they should be advertising on their video.

Chris: The ability to ask questions to the comments section is intriguing. Can you explain how this feature works and provide examples of questions that content creators have found particularly insightful or valuable in understanding their audience?

Griffin: Once we have the body of text that is your comment section, we utilize ChatGPT with some clever prompting to let you ask your questions directly to your comment section. This puts the power into the hands of the user and lets them creatively query their own comments for specific insights. A few useful questions include:

* Are there any requests or suggestions for upcoming videos in the comments?
* What did commenters think of __ in the video?
* Based on the comments what are people engaging with most in the video?

Chris: Are there any plans to expand Comment Explorer’s capabilities beyond YouTube to other platforms where comments play a significant role, such as social media or forums?

Griffin: Currently Comment Explorer plans to focus on Youtube however this concept is certainly extendable to other social media sites such as Instagram, Reddit and Twitter.

Chris: Looking ahead, are there any exciting updates, new features, or developments in the pipeline for Comment Explorer that content creators can look forward to?

Griffin: There are a few updates and feature developments coming down the development pipeline including high-quality comment querying as well as the ability to view the statistics (sentiment, emotion, and subject matter) of individual comments.

Chris: Thanks for being with me, any last words? Where can our readers follow you?

Griffin: Thanks for having me. I post on Twitter from time to time with product updates and updates from other projects I work on. You can check there for product updates and news on other projects I’m working on.