exclusivity: Exclusivity

Description Usage Arguments Details Value References See Also Examples

View source: R/exclusivity.R

Description

Calculate an exclusivity metric for an STM model.

Usage

1
exclusivity(model, M = 10, frexw = 0.7)

Arguments

model

the STM object

M

the number of top words to consider per topic

frexw

the frex weight

Details

In Roberts et al 2014 we proposed using the Mimno et al 2011 semanticCoherence metric for helping with topic model selection. We found that semantic coherence alone is relatively easy to achieve by having only a couple of topics which all are dominated by the most common words. Thus we also proposed an exclusivity measure.

Our exclusivity measure includes some information on word frequency as well. It is based on the FREX labeling metric (calcfrex) with the weight set to .7 in favor of exclusivity by default.

This function is currently marked with the keyword internal because it does not have much error checking.

Value

a numeric vector containing exclusivity for each topic

References

Mimno, D., Wallach, H. M., Talley, E., Leenders, M., & 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. http://goo.gl/0x0tHJ

See Also

searchK plot.searchK semanticCoherence

Examples

1

Example output

stm v1.3.0 (2017-09-08) successfully loaded. See ?stm for help.
[1] 8.451915 8.412544 8.956200

stm documentation built on Dec. 18, 2019, 1:47 a.m.