This project implements a natural language processing system that predicts the category of mobile applications based solely on their description text. Using advanced NLP techniques and deep learning models, the system can automatically classify apps into their appropriate categories in app stores.
Project Overview
The system uses description text from mobile applications to predict which category they belong to in app stores. This has practical applications for app store optimization, content moderation, and recommendation systems.
The project leverages state-of-the-art NLP models from Hugging Face’s Transformers library to achieve high accuracy in category prediction across multiple languages.
Key Features
- Multi-language Support: Works with app descriptions in multiple languages
- Transfer Learning: Utilizes pre-trained transformer models fine-tuned on app descriptions
- High Accuracy: Achieves strong classification performance across different app categories
- Comprehensive Dataset: Trained and evaluated on a large dataset of real app descriptions
- Production-Ready: Includes code for both training and inference
Technical Implementation
The project implements several approaches to app category prediction:
- Fine-tuned ParsBERT and other transformer models
- Text preprocessing optimized for app descriptions
- Evaluation across multiple metrics
- Comparison of different model architectures
Technologies
- Python
- Hugging Face Transformers
- PyTorch
- NLP
- Text Classification
- Deep Learning
- Jupyter Notebook
This project demonstrates how modern NLP techniques can be applied to solve practical classification problems in the mobile app ecosystem, providing valuable tools for app store optimization and content organization.