Typing follows patterns.
People often use predictable word sequences, but most systems do not leverage this effectively.

LinguaNext shows how Natural Language Processing can predict the next word in real time and enhance the typing experience.

The prediction engine uses a Backoff N-Gram Language Model.

• Data sources: Blogs, News, Twitter
• Text cleaning and normalization
• Creation of Unigram, Bigram, and Trigram frequency tables
• Backoff strategy:

Trigram → Bigram → Unigram

This approach balances accuracy and speed.

The model was evaluated using:

• Top-3 next word prediction accuracy
• Perplexity as a language metric
• Response time analysis
• Model size optimization for Shiny deployment

This ensures reliable prediction with fast execution.

A Shiny web application was built to demonstrate the model.

Features include:

• Live text input
• Instant next-word suggestions
• Simple and responsive interface
• Lightweight performance suitable for web deployment

LinguaNext transforms an NLP model into a usable data product.

• Demonstrates real-time prediction
• Easy for anyone to use through a web app
• Practical foundation for smart keyboards and messaging tools