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Emotube

Emotube is a Dockerized web application for analyzing sentiment in YouTube comments, with a React and TypeScript interface, a FastAPI backend, Transformers and PyTorch for sentiment analysis, and PostgreSQL storage.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Emotube — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a self-hosted interface for examining and filtering sentiment in YouTube video comments.

Who it is for

  • People reviewing audience sentiment and comment patterns on YouTube videos.
  • Developers or teams able to configure and run a Docker-based application with a YouTube Data API key.

Documented uses

  • Analyze comments associated with a YouTube video URL.
  • Review positive, negative and neutral sentiment through charts and analytics.
  • Filter comments by sentiment, author, minimum likes or phrase.
Before you choose

Requirements & limits

The documented setup runs the application with Docker Compose and requires a YouTube Data API v3 key.

  • The complete documented platform integration is YouTube.
  • Running the application requires Docker, Docker Compose, a YouTube API key and PostgreSQL configuration.
  • The interface supports sentiment, author, minimum-like and phrase filtering.
  • The repository describes other social-platform integrations as extensibility rather than demonstrated, ready-to-use support.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up Emotube with Docker Compose

Prepare the documented local application configuration and launch its services.

Before you start

  • Docker and Docker Compose
  • A YouTube Data API v3 key
  • The Emotube repository and its .env.example template

Steps

  1. Clone the repository and enter its project directory.

  2. Copy .env.example to .env and configure the YouTube API key, service ports, PostgreSQL settings and frontend backend URL.

  3. Run docker-compose up -d from the project directory.

  4. Open the frontend at http://localhost:3000; the documented backend and API documentation addresses are http://localhost:8000 and http://localhost:8000/docs.

Expected outcome

The documented setup exposes the frontend, backend and API documentation at the listed local addresses.

Guide limitations

  • The guide describes the repository's setup instructions; it does not establish that the services were executed or verified.
  • Analysis is documented for YouTube; additional social-media sources are described as requiring integration.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
00200200/youtube-comment-sentiment-analyzer · Official homepage title · retained in its source language
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