VIDSENTIMENT

Sentiment Analysis of Video Comments

📌 Overview

VIDSENTIMENT is a web-based application that performs sentiment analysis on YouTube video comments. The project leverages machine learning models to classify sentiments and visualize results for better audience insights.

🚀 Features

• Sentiment Classification: Achieved over 85% accuracy in classifying sentiments (positive, neutral, negative).
• YouTube API Integration: Fetches video metadata (title, description, channel name, likes, views) and comments using the YouTube API.
• FastAPI Backend: Provides a scalable and fast API to serve predictions.
• Web Interface: Developed using HTML, CSS, and JavaScript for user-friendly interaction.
• Visualization: Implements graphical bar charts to display sentiment distribution.
• GPT-2 Model: Uses a 123M parameter GPT-2 model for sentiment classification.

🛠️ Tech Stack

• Machine Learning: TensorFlow
• Backend: FastAPI
• Frontend: HTML, CSS, JavaScript
• Cloud Deployment: AWS EC2
• Visualization: Chart.js

📂 Project Structure

VIDSENTIMENT/
│── backend/
│   ├── models/
│   │   ├── Model/
│   │   │   ├── GPT_2.py
│   │   ├── Model Weights/
│   │   │   ├── model_and_optimizer_youtube...
│   ├── routes/
│── frontend/
│   ├── css/
│   │   ├── style.css
│   ├── js/
│   │   ├── script.js
│   ├── index.html
│── scripts/
│   ├── pre_processes.py
│   ├── youtube_data_fetcher.py
│── main.py
│── README.md
│── requirements.txt

🔧 Setup Instructions

1️⃣ Prerequisites

• Python 3.8+
• Google Cloud Console API Key (for YouTube API access)

2️⃣ Installation

Clone the repository and install dependencies:

git clone https://github.com/your-repo/Vidsentiment.git  
cd Vidsentiment  
pip install -r requirements.txt  

3️⃣ Configure API Key

• Get a YouTube API key from Google Cloud Console
• Paste your API key in:

  scripts/pre_processes.py
  

4️⃣ Run the Application

Start the FastAPI backend:

uvicorn main:app --reload

Access the web interface at: http://127.0.0.1:8000/

📊 Impact

• Helps content creators and marketing teams understand audience sentiment.
• Improves engagement strategies and data-driven decision-making.

---

📌 Contributors: Gold Sharon
📌 License: MIT
📌 Contact: [email protected]