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.extract_ingredients.input_model_s3_stm <- function(input_model_s3, n_top_terms = 5, frexweight = .5, use_frex_words = FALSE, need_topic = FALSE, n_topiclabel_words = 8, ...) {
input_model <- input_model_s3$model
K <- input_model$settings$dim$K
V <- input_model$settings$dim$V
if (use_frex_words) {
terms <- stm::labelTopics(input_model, n = input_model$settings$dim$V, frexweight = frexweight)$frex
} else {
terms <- stm::labelTopics(input_model, n = input_model$settings$dim$V)$prob
}
all_terms <- unique(as.vector(terms[,seq_len(n_top_terms)]))
if (need_topic) {
if (use_frex_words) {
model_terms <- stm::labelTopics(input_model, n = n_topiclabel_words, frexweight = frexweight)$frex
} else {
model_terms <- stm::labelTopics(input_model, n = n_topiclabel_words)$prob
}
theta <- input_model$theta
} else {
model_terms <- NULL
theta <- NULL
}
return(list(K = K, V = V, terms = terms, all_terms = all_terms, model_terms = model_terms, theta = theta))
}
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