🧠 Sentiment Analysis of COVID-19 Vaccine Tweets

This project analyzes public sentiment regarding COVID-19 vaccines by classifying tweets into positive, neutral, or negative categories. It applies both machine learning and deep learning models to perform sentiment classification and evaluate their effectiveness.

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📋 Table of Contents

• Dataset
• Prerequisites
• Setup & Installation
• How to Run
• Models Implemented
• Results & Conclusion
• Author

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📂 Dataset

• Dataset used: covid-19_vaccine_tweets_with_sentiment.csv
• Ensure this file is placed in the root directory of the project for successful execution.

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⚙️ Prerequisites

Before starting, make sure the following are installed:

• Python 3.8+
• Pip package manager
• Jupyter Notebook or JupyterLab

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🧰 Setup & Installation

# 1️⃣ Clone the repository
git clone <your-repository-url>
cd <repository-name>

# 2️⃣ Create and activate a virtual environment
# For Windows
python -m venv venv
.\venv\Scripts\activate

# For macOS/Linux
python3 -m venv venv
source venv/bin/activate

# 3️⃣ Install dependencies
pip install pandas scikit-learn matplotlib tensorflow


Then open the file ML_mini_project.ipynb (or your chosen filename).

Run the cells sequentially from top to bottom.

The notebook covers:

  📥 Data loading and exploration

  🧹 Text preprocessing and cleaning

  ✂️ Train-test split

  🧠 Feature extraction using TF-IDF

  ⚙️ Model training and evaluation

  📊 Visualization of performance metrics