Around the world, people are spending an increasing amount of time on their mobile devices for email, social networking, banking and a whole range of other activities. But typing on mobile devices can be a serious pain. SwiftKey, for example, builds a smart keyboard that makes it easier for people to type on their mobile devices. One cornerstone of their smart keyboard is predictive text models. When someone types:
I went to the
the keyboard presents three options for what the next word might be. For example, the three words might be gym, store, restaurant.
As part of my capstone project — with Swiftkey as the corporate partner — for the Data Science Specialization offered by John Hopkins University through Coursera, I have analyzed a large corpus of text documents to discover the structure in the data and how words are put together to build and sample from a predictive text model. This brings you a predictive text product hosted on shinyapps.io:
The app takes as input a phrase (multiple words), one clicks submit, and it predicts the next word.
For more information on how the app works under the hood, please see:
- 5-slide intro: slide-deck on RPubs.
- Summary report of the data: comprises of tweets, news, and blogs in English.
- Source codes on github.