Predictive Text Model: Next-Word Prediction App

Problem and Opportunity

Text prediction powers modern communication tools. From messaging apps to search engines, smart autocomplete makes writing faster and easier. The goal is to develop a lightweight predictive text model using R and deploy it with a user-friendly Shiny interface.

Algorithm Overview

• Model Type: Bigram-based word prediction
• Training Data: Cleaned corpus from Twitter and news sources
• Preprocessing: Tokenization, lowercasing, removal of punctuation and stopwords
• Prediction Logic: Match the final word of a phrase with a bigram frequency table and return the most probable next word
• Future Enhancements: Trigram and neural models (LSTM, Transformer)

App Demo and Instructions

Hosted at: https://polarbear244.shinyapps.io/project-1

1. Enter a phrase into the input box
2. Click the Predict button
3. The most likely next word is displayed

Applications: Writing support tools, educational resources, social media automation

Business Value

• Faster typing improves productivity
• Adaptable to specific domains (e.g., medical, legal)
• Scalable architecture and commercial deployment opportunities
• Potential integrations with content management systems and mobile platforms

Why Me

• Developed and deployed the model and Shiny interface independently
• Demonstrated full-stack R development and presentation
• Ready to optimize and expand the tool's capabilities
• Dedicated to building practical natural language applications