This project was capstone project for Coursera Data Science Specialization held by Johns Hopkins University. SwiftKey is corporate partner in this project.

The purpose of this project is to learn and practice text mining and NLP knowledge and skills. We’ll start from text dataset provided by SwiftKey, build up algorithms to predict next most likely word following given text. A Shiny application will be created to accept user input and display predicted word.

Algorithm Description

After random sampling the text data set, a corpus data was created and cleaned. Data cleaning approaches include: change to lower case, remove number, remove punctuation, strip whitespaces, remove English end words.

Based on this, tokenization was conducted to create bi-grams and tri-grams data matrixes.

For example,
Top 5 bi-gram words are: “right now”, “dont know”, “last year”, “new york”, “cant wait”
Top 5 tri-gram words are: “happy mothers day”, “cant wait see”, “new york city”, “let us know”, “world war ii”

Furthermore, frequency data tables are created respectively and thus being used for prediction.

Shiny App Description

This app accept user input text at left sidebar and display the predicted next word on right panel. For example, input “happy” and press Enter, “birthday” will be predicted. Input “let us”, “know” will be predicted.
The app takes input words and match them in frequency data table to find the most likely next word.

• App is hosted on Shiny server
• Source code on Github