Youtube video comment analysis

An automation tool that analyzes comments from a specified YouTube video to answer user-posed questions.


The Youtube video comment analysis tool is a sophisticated yet user-friendly solution that leverages the power of AI to sift through the plethora of comments on a YouTube video, providing insights into the collective sentiment or specific queries posed by users. It operates on a low-code AI workflow platform, making it accessible to a broad audience without the need for extensive technical expertise. The tool's core functionality revolves around extracting comments using a Python script and then employing a state-of-the-art language model to interpret these comments and deliver precise answers to the questions asked by the user.

Use cases

Marketing professionals can use this tool to measure the impact of their video content by understanding viewer sentiment. Content creators might employ it to identify frequently asked questions or common themes in viewer feedback, which can inform future content creation. Researchers could leverage the tool to conduct qualitative analysis on public opinion regarding a specific topic discussed in a video.


The primary benefit of the Youtube video comment analysis tool is its ability to transform unstructured data from YouTube comments into actionable insights. Users can gauge public opinion, gather feedback, or clarify doubts by tapping into the collective intelligence of the commenters. This tool eliminates the manual effort of reading through comments and provides a quick, automated, and accurate analysis.

How it works

To initiate the process, the user inputs the YouTube video URL and their question into the tool. A Python script then downloads the comments from the video, sorting them according to a predefined criterion. Following this, the tool uses an advanced language model, specifically openai-gpt3.5-16k, to analyze the comments. The model receives a prompt that includes the comments and the user's question, and it generates an answer with a deterministic approach, owing to a temperature setting of zero. The outcome is a well-structured response that directly addresses the user's inquiry, based on the collective input from the video's comment section.

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