2025-04-13

The Problem

  • In a fast-paced world, typing efficiency matters.
  • Users want faster, smarter typing experiences on mobile and desktop.
  • Predictive text models are already shaping communication via AI keyboards.
  • But building one requires clean NLP pipelines, n-gram modeling, and reactive UIs.

The Solution

  • We built a Shiny-powered Next-Word Predictor App.
  • Trained on a large corpus (Twitter, Blogs, News).
  • Leverages n-gram models (uni-, bi-, tri-grams) to predict the next word based on context.
  • Real-time word prediction triggered by input text.

How It Works

  • Text preprocessing: cleaning, tokenization, and stopword removal.
  • N-gram generation: from millions of text lines to word pattern frequencies.
  • Prediction logic:
    • If 3-gram match found → use it
    • Else try 2-gram → else 1-gram fallback
  • Built with tm, tidytext, and shiny

Why It Matters

  • Functional demo deployed at shinyapps.io
  • Helps users compose texts/emails faster
  • Could be extended to multilingual use, SMS auto-completion, or education
  • Lightweight, fast, and built for real users with scalable architecture