SenaL - Flask Based Sentiment Analysis

📊🔎💬🚀

SenaL is a powerful sentiment analysis tool built on Flask, a micro web framework for Python, that provides users with an intuitive user interface to analyze the sentiment of textual data. By utilizing the Vader (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis tool, SenaL allows users to gain valuable insights into the emotions and sentiments expressed in social media texts, tweets, captions, comments, and other forms of text.

Demo

🎉 Check out the live demo of SenaL and try out the sentiment analysis on different texts!

Deployement on AWS EC2
Deployement on Render

Features

1. Sentiment Analysis: 🔍 SenaL leverages the Vader sentiment analysis tool to accurately determine the sentiment polarity of text. Whether it's positive, negative, or neutral, SenaL provides users with valuable information on the emotional content of the text.

2. User-Friendly Interface: 🚀 SenaL offers a clean and user-friendly interface, making it easy for users to interact with the sentiment analysis tool. The intuitive design ensures a smooth user experience, allowing users to input their text and obtain sentiment analysis results effortlessly.

3. Social Media Analysis: 💬 SenaL specializes in sentiment analysis of social media texts, tweets, captions, comments, and more. This feature enables users to gather and collect emotions expressed in social media platforms, facilitating better understanding and analysis of user sentiment.

4. Minimal Dependencies: 📦 SenaL requires only a few essential libraries to function, including Flask for web application development, vaderSentiment for sentiment analysis, nltk for natural language processing, scikit-learn for machine learning, and requests for making HTTP requests. This minimal dependency footprint ensures easy integration and smooth execution.

How to Use SenaL

1. Install Dependencies: 🛠️ Ensure that Flask, vaderSentiment, nltk, scikit-learn, and requests libraries are installed in your Python environment.

2. Clone the Repository: 📥 Clone the SenaL repository from GitHub to your local machine using the following command:

git clone https://github.com/KartikeyMish/SenaL.git

3. Set Up Environment: 🌐 Navigate to the project directory and create a virtual environment to isolate project dependencies. Activate the virtual environment.

4. Install Additional Dependencies: ⬇️ Install any additional dependencies required by SenaL using the following command:

pip install -r requirements.txt

5. Start the Application: ▶️ Run the Flask application using the following command:

python app.py

6. Access SenaL: 🌐 Open a web browser and go to http://localhost:5000 to access the SenaL user interface. Enter the text you want to analyze and click the "Submit" button to obtain sentiment analysis results.

Docker 🐳

The project is dockerized for easy deployment and scalability. To run the app using Docker, use the following commands:

1. Build the image

docker build --pull --rm -f "Dockerfile" -t senal:latest "."

2. Run image

docker run --rm -d -p 80:80/tcp senal:latest 

Contributing

🤝 Contributions to SenaL are welcome! If you encounter any bugs, issues, or have suggestions for improvements, please submit a GitHub issue. You can also submit pull requests with your proposed changes to contribute to the project.

Contact

📧 For any inquiries or further information about SenaL, please contact us at Kartikey Mishra