tidylo: Weighted Tidy Log Odds Ratio

Authors: Julia Silge, Alex Hayes, Tyler Schnoebelen
License: MIT

How can we measure how the usage or frequency of some feature, such as words, differs across some group or set, such as documents? One option is to use the log odds ratio, but the log odds ratio alone does not account for sampling variability; we haven't counted every feature the same number of times so how do we know which differences are meaningful?

Enter the weighted log odds, which tidylo provides an implementation for, using tidy data principles. In particular, here we use the method outlined in Monroe, Colaresi, and Quinn (2008) to weight the log odds ratio by a prior. By default, the prior is estimated from the data itself, an empirical Bayes approach, but an uninformative prior is also available.

Installation

You can install the released version of tidylo from CRAN with:

install.packages("tidylo")

Or you can install the development version from GitHub with remotes:

library(remotes)
install_github("juliasilge/tidylo", ref = "main")

Example

Using weighted log odds is a great approach for text analysis when we want to measure how word usage differs across a set of documents. Let's explore the six published, completed novels of Jane Austen and use the tidytext package to count up the bigrams (sequences of two adjacent words) in each novel. This weighted log odds approach would work equally well for single words.

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(janeaustenr)
library(tidytext)

tidy_bigrams <- austen_books() %>%
     unnest_tokens(bigram, text, token = "ngrams", n = 2)

bigram_counts <- tidy_bigrams %>%
     count(book, bigram, sort = TRUE)

bigram_counts
#> # A tibble: 328,495 x 3
#>    book                bigram     n
#>    <fct>               <chr>  <int>
#>  1 Mansfield Park      of the   748
#>  2 Mansfield Park      to be    643
#>  3 Emma                to be    607
#>  4 Mansfield Park      in the   578
#>  5 Emma                of the   566
#>  6 Pride & Prejudice   of the   464
#>  7 Emma                it was   448
#>  8 Emma                in the   446
#>  9 Pride & Prejudice   to be    443
#> 10 Sense & Sensibility to be    436
#> # … with 328,485 more rows

Now let's use the bind_log_odds() function from the tidylo package to find the weighted log odds for each bigram. The weighted log odds computed by this function are also z-scores for the log odds; this quantity is useful for comparing frequencies across categories or sets but its relationship to an odds ratio is not straightforward after the weighting.

What are the bigrams with the highest weighted log odds for these books?

library(tidylo)

bigram_log_odds <- bigram_counts %>%
  bind_log_odds(book, bigram, n) 

bigram_log_odds %>%
  arrange(-log_odds_weighted)
#> # A tibble: 328,495 x 4
#>    book                bigram                n log_odds_weighted
#>    <fct>               <chr>             <int>             <dbl>
#>  1 Mansfield Park      sir thomas          287              28.3
#>  2 Pride & Prejudice   mr darcy            243              27.7
#>  3 Emma                mr knightley        269              27.5
#>  4 Emma                mrs weston          229              25.4
#>  5 Sense & Sensibility mrs jennings        199              25.2
#>  6 Persuasion          captain wentworth   170              25.1
#>  7 Mansfield Park      miss crawford       215              24.5
#>  8 Persuasion          mr elliot           147              23.3
#>  9 Emma                mr elton            190              23.1
#> 10 Emma                miss woodhouse      162              21.3
#> # … with 328,485 more rows

The bigrams more likely to come from each book, compared to the others, involve proper nouns. We can make a visualization as well.

library(ggplot2)

bigram_log_odds %>%
  group_by(book) %>%
  top_n(10) %>%
  ungroup %>%
  mutate(bigram = reorder(bigram, log_odds_weighted)) %>%
  ggplot(aes(bigram, log_odds_weighted, fill = book)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~book, scales = "free") +
  coord_flip() +
  labs(x = NULL)
#> Selecting by log_odds_weighted

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