Coursera Data Science Specialication Capstone Project

The objective of the assignment:

• Develop a shiny app in which the next word is predicted based on text input.

My approach:

• By exploring the given data sets, I found that the frequency distributions of words used in Twitter are different from that in blogs and news articles. Therefore, I built a prediction model specialized for Twitter.
• The prediction model takes into account not only the frequency of each word used in Twitter, but also sequences of words consist of multiple words (n-gram words). In this model, bigram, trigram, and quadrigram words are considered.

The application is available here: https://cat-fish.shinyapps.io/shiny2/ How to use?

• Input words in “Input text here” box
• The predicted next word appears in “Predicted word” box!

What is special? This app has some unique features…

• Input one of seven dirty words… What you do get in the prediction box?
• Input a long sentence. Do you see the algorithm behind it?

Treatment of swear words

• I believe that not swear words themselves but the usage of words matter. Therefore, I arranged the model to show warnings whenever one of seven dirty words is used.

Methodology to improve the prediction accuracy

• When more than 3 words are given, the last 3 words are used to predict the next word (because this model considers maximum 4-gram words). If the sequence of the 3 words does not match with any 4-gram words in the database, the last 2 words are now used to match with 3-gram words in the database. The same process is done for 2 words.
• If no match is found, one of the most frequently used words in the database is randomly given.

• The accuracy of the model is expected to be improved by using larget data sets to build a word frequency database.
• Removing affiexes from words to make a word frequency database could be done in a better way.
• It would be great if emoji and emoticon could be taken into account.