Calculate lexical similarity for multiple dyads — lexical_sim

This function calculates lexical similarity over a sequence of conversation exchanges for multiple dyads.

Usage

lexical_sim_dyads(conversations, window_size = 3)

Arguments

conversations: A data frame with columns 'dyad_id', 'speaker', and 'processed_text'
window_size: An integer specifying the size of the sliding window

Value

A list containing the sequence of similarities for each dyad and the overall average similarity

Examples

library(lme4)
#> Loading required package: Matrix
convs <- data.frame(
  dyad_id = c(1, 1, 1, 1, 2, 2, 2, 2),
  speaker = c("A", "B", "A", "B", "C", "D", "C", "D"),
  processed_text = c("i love pizza", "me too favorite food",
                     "whats your favorite topping", "enjoy pepperoni mushrooms",
                     "i prefer pasta", "pasta delicious like spaghetti carbonara",
                     "ever tried making home", "yes quite easy make")
)
lexical_sim_dyads(convs, window_size = 2)
#> boundary (singular) fit: see help('isSingular')
#> $similarities_by_dyad
#> $similarities_by_dyad$`1`
#> [1] 0.0000000 0.1428571 0.0000000
#> 
#> $similarities_by_dyad$`2`
#> [1] 0.1428571 0.0000000 0.0000000
#> 
#> 
#> $overall_average
#> (Intercept) 
#>  0.04761905 
#>