Calculate topic similarity for multiple dyads — topic_sim

This function calculates topic similarity over a sequence of conversation exchanges for multiple dyads. It uses the Latent Dirichlet Allocation (LDA) method for topic modeling and the "slam" package for efficient handling of sparse matrices.

Usage

topic_sim_dyads(conversations, method = "lda", num_topics = 2, window_size = 3)

Arguments

conversations: A data frame with columns 'dyad_id', 'speaker', and 'processed_text'
method: A character string specifying the method to use: currently only "lda" is supported
num_topics: An integer specifying the number of topics to use in the LDA model
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")
)
topic_sim_dyads(convs, method = "lda", num_topics = 2, window_size = 2)
#> $similarities_by_dyad
#> $similarities_by_dyad$`1`
#> [1] 0 0 0
#> 
#> $similarities_by_dyad$`2`
#> [1] 0 0 0
#> 
#> 
#> $overall_average
#> (Intercept) 
#>           0 
#>