Description Usage Arguments Value References See Also Examples
View source: R/topic_exclusivity.R
Using the the N highest probability tokens for each topic, calculate the exclusivity for each topic
1  topic_exclusivity(topic_model, top_n_tokens = 10, excl_weight = 0.5)

topic_model 
a fitted topic model object from one of the following:

top_n_tokens 
an integer indicating the number of top words to consider, the default is 10 
excl_weight 
a numeric between 0 and 1 indicating the weight to place on exclusivity versus frequency in the calculation, 0.5 is the default 
A vector of exclusivity values with length equal to the number of topics in the fitted model
Bischof, Jonathan, and Edoardo Airoldi. 2012. "Summarizing topical content with word frequency and exclusivity." In Proceedings of the 29th International Conference on Machine Learning (ICML12), eds John Langford and Joelle Pineau.New York, NY: Omnipress, 201–208.
1 2 3 4 5  # Using the example from the LDA function
library(topicmodels)
data("AssociatedPress", package = "topicmodels")
lda < LDA(AssociatedPress[1:20,], control = list(alpha = 0.1), k = 2)
topic_exclusivity(lda)

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