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Calculate stylistic similarity for multiple dyads

Source: R/conversation_multidyads.R
stylistic_sim_dyads.Rd

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

Usage

stylistic_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

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")
)
stylistic_sim_dyads(convs, window_size = 2)
#> boundary (singular) fit: see help('isSingular')
#> $similarities_by_dyad
#> $similarities_by_dyad$`1`
#> [1] 0.7230209 1.0000000 0.7822529
#> 
#> $similarities_by_dyad$`2`
#> [1] 0.6058412 0.7322554 1.0000000
#> 
#> 
#> $overall_average
#> (Intercept) 
#>   0.8072284 
#>