sts (version 1.3)

topicExclusivity: Compute Exclusivity

Description

Calculate an exclusivity metric for an STS model.

Usage

topicExclusivity(object, M = 10, frexw = 0.7)

Value

a numeric vector containing exclusivity for each topic

Arguments

object

Model output from sts

M

the number of top words to consider per topic

frexw

the frex weight

Details

Roberts et al 2014 proposed an exclusivity measure to help with topic model selection.

The exclusivity measure includes some information on word frequency as well. It is based on the FREX labeling metric (see Roberts et al. 2014) with the weight set to .7 in favor of exclusivity by default.

References

Mimno, D., Wallach, H. M., Talley, E., Leenders, M., and McCallum, A. (2011, July). "Optimizing semantic coherence in topic models." In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 262-272). Association for Computational Linguistics. Chicago

Bischof and Airoldi (2012) "Summarizing topical content with word frequency and exclusivity" In Proceedings of the International Conference on Machine Learning.

Roberts, M., Stewart, B., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S., Albertson, B., et al. (2014). "Structural topic models for open ended survey responses." American Journal of Political Science, 58(4), 1064-1082.

Examples

 # \donttest{
#An example using the Gadarian data from the stm package.  
# From Raw text to fitted model using textProcessor() which leverages the 
# tm Package
library("tm"); library("stm"); library("sts")
temp<-textProcessor(documents=gadarian$open.ended.response,
metadata=gadarian, verbose = FALSE)
out <- prepDocuments(temp$documents, temp$vocab, temp$meta, verbose = FALSE)
out$meta$noTreatment <- ifelse(out$meta$treatment == 1, -1, 1)
## low max iteration number just for testing
sts_estimate <- sts(~ treatment*pid_rep, ~ noTreatment, out, K = 3, maxIter = 2)
topicExclusivity(sts_estimate)
# }