Calculate sentiment similarity for multiple dyads — sentiment_sim

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

Usage

sentiment_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)
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")
)
sentiment_sim_dyads(convs, window_size = 2)
#> $similarities_by_dyad
#> $similarities_by_dyad$`1`
#> [1] 0.9290064 0.9000000 0.9709936
#> 
#> $similarities_by_dyad$`2`
#> [1] 0.8052607 0.7763932 0.2800000
#> 
#> 
#> $overall_average
#> (Intercept) 
#>   0.7769423 
#>