Introduction

I often see advertisement for The Bold and The Beautiful, I have never watched a single episode of the series. Still, even as a data scientist you might be wondering how these beautiful ladies and gentlemen from the show are related to each other. I do not have the time to watch all these episodes to find out, so I am going to use word embeddings on recaps instead…

Calculating word embeddings

First, we need some data, from the first few google hits I got to the site soap central. Recaps can be found from the show that date back to 1997. Then, I used a little bit of rvest code to scrape the daily recaps into an R data set.

Word embedding is a technique to transform a word onto a vector of numbers, there are several approaches to do this. I have used the so-called Global Vector word embedding. See here for details, it makes use of word co-occurrences that are determined from a (large) collection of documents and there is fast implementation in the R text2vec package.

Once words are transformed to vectors, you can calculate distances (similarities) between the words, for a specific word you can calculate the top 10 closest words for example. More over linguistic regularities can be determined, for example:

amsterdam - netherlands + germany

would result in a vector that would be close to the vector for berlin.

Results for The B&B recaps

It takes about an hour on my laptop to determine the word vectors (length 250) from 3645 B&B recaps (15 seasons). After removing some common stop words, I have 10.293 unique words, text2vec puts the embeddings in a matrix (10.293 by 250).

Lets take the lovely steffy,

the ten closest words are:

    from     to     value
    <chr>  <chr>     <dbl>
 1 steffy steffy 1.0000000
 2 steffy   liam 0.8236346
 3 steffy   hope 0.7904697
 4 steffy   said 0.7846245
 5 steffy  wyatt 0.7665321
 6 steffy   bill 0.6978901
 7 steffy  asked 0.6879022
 8 steffy  quinn 0.6781523
 9 steffy agreed 0.6563833
10 steffy   rick 0.6506576

Lets take take the vector steffy – liam, the closest words we get are

       death     furious      lastly     excused frustration       onset 
   0.2237339   0.2006695   0.1963466   0.1958089   0.1950601   0.1937230 

 and for bill – anger we get

     liam     katie     wyatt    steffy     quinn      said 
0.5550065 0.4845969 0.4829327 0.4645065 0.4491479 0.4201712 

The following figure shows some other B&B characters and their closest matches.

If you want to see the top n characters for other B&B characters use my little shiny app. The R code for scraping B&B recaps, calculating glove word-embeddings and a small shiny app can be found on my Git Hub.

Conclusion

This is a Mickey Mouse use case, but it might be handy if you are in the train and hear people next to you talking about the B&B, you can join their conversation. Especially if you have had a look at my B&B shiny app……

Cheers, Longhow