🐦 Tweet Sentiment Analysis Web App

This is a simple Flask-based web application that uses the Twitter API v2 to fetch recent tweets based on a keyword and performs sentiment analysis using TextBlob. It classifies each tweet as Positive, Negative, or Neutral.

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🔧 Features

• Fetches recent tweets using Twitter API v2
• Performs sentiment analysis using TextBlob
• Displays results in a clean and responsive web interface
• Built with Flask, Tweepy, and Jinja2

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🧰 Technologies Used

• Python 3.x
• Flask
• Tweepy (Twitter API v2)
• TextBlob
• dotenv (python-dotenv)
• HTML (Jinja2 templating)

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📦 Installation

1. Clone the repository

git clone https://github.com/Chirag-joshi123/tweet-sentiment-analysis.git
cd tweet-sentiment-analysis

2. Create a virtual environment and activate it (optional but recommended)

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Set up your environment variables

Create a .env file in the root directory:

touch .env

Inside .env, add your Twitter Bearer Token:

BEARER_TOKEN=your_twitter_api_bearer_token_here

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🔑 How to Get Your Twitter API Bearer Token

1. Go to the Twitter Developer Portal

2. Log in with your Twitter account and click on Developer Portal

3. Click Projects & Apps > Overview > + Create App

4. After creating the app:

   • Go to your App Settings
   • Navigate to Keys and Tokens
   • Copy the Bearer Token and paste it into your .env file

Make sure to keep it private and never expose it in public repositories.

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🚀 Running the App Locally

python app.py

Then open your browser and go to: 👉 http://127.0.0.1:5000

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🌍 Deploying on Render (Free Hosting)

Render makes it easy to deploy Flask apps.

Step-by-step:

1. Push your code to GitHub

2. Go to https://render.com and sign in

3. Click on "New Web Service"

4. Connect your GitHub repo and select this project

5. Use the following settings:

   • Build Command:

     pip install -r requirements.txt
     

   • Start Command:

     gunicorn app:app
     

   • Runtime: Python 3.x

   • Environment Variables: Add your BEARER_TOKEN key here.

6. Click "Create Web Service"

Your app will be live at a Render-provided URL!

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📄 File Structure

tweet-sentiment-analysis/
├── app.py               # Main Flask app
├── templates/
│   └── index.html       # HTML template for UI
├── .env                 # Environment variables (not to be shared)
├── requirements.txt     # Python dependencies
├── README.md            # Project documentation
└── Procfile             # Optional (for deployment platforms like Heroku/Render)

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📌 Notes

• Only tweets in English (lang='en') are analyzed.
• The app fetches a maximum of 10 tweets per query to respect API rate limits.
• Twitter API v2 Bearer Token is required — you can get one by creating a project on the Twitter Developer Portal.

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💡 Future Improvements

• Support for analyzing tweet trends
• Graphical visualization of sentiment
• OAuth login for posting or interacting with tweets
• Support for multiple languages

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📝 License

This project is open-source under the MIT License.

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🙋‍♂️ Author

Chirag Joshi – @Chirag-joshi123 Feel free to fork, contribute, or reach out with suggestions!