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Social Video Audience Sentiment Intelligence

Develop an intelligent system to analyze audience sentiment from social video comments. The project focuses on collecting, preprocessing, and classifying user comments to understand overall sentiment trends and provide actionable insights for content creators.

22 lectures

What You Will Learn

Mastering data collection techniques for extracting social media video comments.
Implementing data preprocessing pipelines for cleaning and preparing text data.
Building machine learning models for accurate sentiment classification.
Utilizing MLflow for experiment tracking and model management.
Deploying machine learning models as RESTful APIs using Flask.
Containerizing applications using Docker for consistent deployment environments.
Implementing CI/CD pipelines on AWS for automated deployment

System Architecture

Social Video Audience Sentiment Intelligence Architecture Diagram

High-level architecture overview of the Social Video Audience Sentiment Intelligence .

What You'll Build

  • A "Social Video Audience Sentiment Intelligence" system capable of analyzing sentiment from YouTube comments.
  • An automated ML Pipeline that handles data ingestion, preprocessing, and model training
  • A Prediction API exposed via a Flask web application for real-time sentiment analysis.
  • A Chrome Extension that allows users to analyze sentiment directly on YouTube video pages.
Social Video Audience Sentiment Intelligence
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