Sports News Text Classifier

Modular Python application for classifying sports news articles into previews and match reports using natural language processing (NLP) and deep learning techniques.

The project is designed for academic purposes and combines advanced Python programming concepts with neural network–based text classification. It provides both a command-line workflow and a graphical user interface (GUI) for interactive testing.

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Project Overview

The goal of this project is to automatically distinguish between:

• Preview articles – texts describing upcoming sports events
• Report articles – texts summarizing already played matches

The system processes raw text input, applies NLP preprocessing, and uses a trained LSTM neural network to predict the article category along with a confidence score.

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Key Features

• Modular project architecture (models, training, utilities, GUI)
• Text preprocessing and tokenization
• Deep learning model (LSTM) implemented using PyTorch
• Train / validation / test split with proper evaluation
• F1-macro metric to address class imbalance
• Saved trained model for immediate inference
• Graphical user interface for user interaction
• Unit tests and doctests
• Use of advanced Python programming techniques:
  • decorators
  • closures
  • abstract base classes (ABC)
  • protocols
  • concurrency (ThreadPoolExecutor / asyncio)
  • type annotations (PEP484, PEP526)

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Technologies Used

• Python 3.11
• PyTorch
• Tkinter
• scikit-learn
• NumPy
• pandas

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Project Structure

textClassifier/
│
├── models/ # Baseline and neural network models
├── training/ # Training, evaluation, dataset splitting
├── utils/ # Preprocessing, decorators, concurrency utilities
├── gui/ # Graphical user interface
├── tests/ # Unit tests
├── saved_models/ # Trained LSTM model
├── requirements.txt
└── README.md

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Installation

It is recommended to use a virtual environment.

python -m venv venv
source venv/bin/activate      # Linux / macOS
venv\Scripts\activate         # Windows

Install the required dependencies:

pip install -r requirements.txt

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Training the Model

From the project root directory, run:

python -m training.train

The model with the best validation F1-macro score is automatically saved

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Model Evaluation

To evaluate the trained model on the test dataset:

python -m training.evaluate

The script prints accuracy, F1-macro score, and a detailed classification report

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Running the GUI

Launch the graphical user interface with:

python -m gui.app

Enter a sports news article into the text field to obtain the predicted class and confidence score

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Testing

Run unit tests:

python -m unittest discover -s tests -v

Run doctests:

python -m doctest -v utils/preprocessing.py

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Notes

Due to the small dataset size and heuristic labeling, evaluation results may exhibit higher variance

F1-macro is used as the primary evaluation metric because of class imbalance

The project is intended for educational and academic use

License

This project is provided for academic purposes only