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Data Science · Mini Project

YouTube Comment Sentiment Analysis

Pulls comments via the YouTube Data API and charts audience mood

1-2 weeks build

Overview

Paste a video link and the app fetches its public comments through the official YouTube Data API v3, paging through results within the free quota. Comments are cleaned, language-filtered and scored with a pretrained multilingual sentiment model, then shown in a Streamlit dashboard with sentiment split, a timeline, top keywords and the most-liked positive and negative comments.

What makes it stand up

  • 01Official YouTube Data API v3 client with quota-aware paging
  • 02Emoji, URL and spam cleanup before scoring
  • 03Pretrained transformer sentiment with a VADER comparison
  • 04Sentiment over time since the video was published
  • 05Two-video comparison mode in the dashboard

Module breakdown

  1. 01

    Collector

    Fetches comment threads and replies through the API.

  2. 02

    Cleaning

    Strips links, repeated characters and obvious spam.

  3. 03

    Scoring

    Runs batched sentiment inference and stores results.

  4. 04

    Keywords

    Extracts frequent terms per sentiment class.

  5. 05

    Dashboard

    Streamlit charts, filters and CSV download.

After this, you will be able to

  • Explain API keys, quotas and pagination in the Data API
  • Defend why a pretrained model suits short, noisy comments
  • Demo analysis of a video chosen by the examiner

Technology stack

  • Python
  • YouTube Data API
  • Pandas
  • Hugging Face Transformers
  • Streamlit

Complete kit

₹5,499₹9,299
-41%
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Delivered in under 48 hours

Level
Mini Project
Domain
Data Science
Build time
1-2 weeks
Difficulty

You receive

  • Complete, commented source code
  • 60-90 page project report (IEEE format)
  • Editable presentation deck
  • Architecture, ER and UML diagrams
  • Local setup and deployment guide
  • Sample dataset or seed data
  • Viva question bank with answers
  • One 45-minute walkthrough call
  • 30 days of doubt-clearing support
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