Natural Language Processing Data Product

Objective

• Predict the next word in an English phrase.
• Build an interactive Shiny application using NLP.
• Use English Blogs, News, and Twitter datasets.

Product

An interactive application where users enter a phrase and receive one predicted next word.

Prediction Algorithm

How the Model Works

1. User enters a phrase.
2. The model checks the word patterns.
3. It first searches for a matching trigram.
4. If unavailable, it uses a bigram.
5. One next word is returned.

Model Summary

• Bigram patterns: 84,287
• Trigram patterns: 54,680

Shiny Application

How It Works

• Enter a multiple-word English phrase.
• Click Predict Next Word.
• Receive one predicted word.

Example

Input: I am going

Prediction: to

The app provides a simple and interactive interface.

Results and User Experience

Key Findings

• The app successfully generates predictions.
• It accepts multiple-word phrases.
• It returns a single predicted word.
• Trigram prediction is used first.
• Bigram prediction provides a fallback.

User Experience

Enter phrase → Predict → Receive one word

Conclusion and Future Improvements

Conclusion

• Built a working NLP prediction model.
• Used Bigram and Trigram patterns.
• Developed an interactive Shiny application.

Future Improvements

• Use more training data.
• Improve prediction accuracy.
• Handle uncommon words better.
• Improve prediction speed.
• Explore advanced language models.

Final Takeaway

A simple NLP model can provide an interactive next-word prediction experience.