College Menfess Tweet Emotion Project

This project is a Flask web application that predicts the emotions or sentiment of tweets sent to a college menfess account. The application uses machine learning models to analyze and classify the tweets.

Features

• Emotion Prediction: Predicts the emotion of a tweet using a custom prediction function.
• Support Vector Machine (SVM) Prediction: Provides an additional prediction using a Support Vector Machine (SVM) model.

Project Structure

.
├── main.py              # The main Flask application script
├── script/
│   └── func.py          # Contains the function `predictTweet` used for predicting the tweet emotion
├── kmeans.py            # Contains the functions `predict_label` and `predict_label_svm` for SVM prediction
├── templates/
│   └── ...              # Directory for HTML templates (if any)
└── README.md            # Project documentation

Getting Started

Prerequisites

• Python 3.x
• Flask
• NumPy
• Pickle (for model loading)

Installation

1. Clone the repository:

   git clone https://github.com/akhmadramadani/collegemenfess-emotion.git
   cd collegemenfess-emotion

2. Install the required Python packages:

   pip install -r requirements.txt

Usage

1. Run the Flask application:

   python main.py

2. The application will start on http://127.0.0.1:5000/ by default.

API Endpoints

• GET /: Returns a simple greeting message.

• POST /predict: Predicts the emotion of the provided tweet. Requires the following form data:

  • tweet: The tweet text to be analyzed.

  Example request:

  curl -X POST -F 'tweet=Your tweet text here' http://127.0.0.1:5000/predict

  Example response:

  {
    "prediction": "positive",
    "predict_using_svm": "neutral"
  }

• POST /predict_svm: Predicts the emotion using the SVM model only. Requires the following form data:

  • tweet: The tweet text to be analyzed.

  Example request:

  curl -X POST -F 'tweet=Your tweet text here' http://127.0.0.1:5000/predict_svm

  Example response:

  {
    "prediction": "neutral"
  }

Environment Variables

• PORT: Set the port for the Flask application. Default is 5000.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please submit a pull request or open an issue for any feature requests or bug reports.

Authors

• Akhmad Ramadani - Github

This `README.md` file provides a clear overview of the project, instructions on how to set it up, and details about the API endpoints. Be sure to replace placeholders like `Your Name` and `Your GitHub` with the appropriate details for your project.