Future Text Predictor: Capstone Project

Enhancing typing efficiency through predictive text suggestions using NLP techniques.

• Objective: To streamline text input by predicting the next word, reducing typing time and effort.
• Technology: Utilizes advanced Natural Language Processing algorithms for accurate and context-aware predictions.
• User Impact: Aims to improve communication efficiency for users across various applications, from messaging to professional writing.

Objective and Features

Objective:

• Enhance typing efficiency with predictive text suggestions.

• Utilize NLP techniques for dynamic predictions.

Features:

• Predicts the next word based on user input.

• Real-time interaction via a user-friendly interface.

• Built with R, Shiny, and a large corpus of text.

Data and Methodology

Dataset:

• Sources: Blogs, news articles, and tweets.

• Size: Over 3 million lines of text.

• Sampled: 100,000 lines for efficient processing.

Methodology:

• Data Preprocessing:

  • Removed special characters and normalized text.

• N-gram Modeling:

  • Generated uni-, bi-, and tri-grams.

  • Implemented back-off models for unseen word predictions.

Algorithm Highlights

Algorithm:

• N-gram Model- Predicts based on frequency analysis of word sequences.

• Back-off Mechanism- Reduces reliance on higher-order n-grams if data is sparse.

• Smoothing- Handles rare or unseen combinations efficiently.

Challenges:

• Memory and Speed- Optimized by sampling datasets and modular coding.

Demonstration and Future Scope

Demo:

• Interactive Shiny app for real-time text prediction.

• Deployed online for accessibility.

• Explore the app: Future Text Predictor

Future Enhancements:

• Advanced NLP models like Transformers for higher accuracy.

• Multilingual support.

• Expand deployment to mobile and desktop platforms.