I'm using the topicmodels package for R to cluster a big set of short texts (between 10-75 words) into topics. After manually reviewing a few models it seems like there are 20 realtivly stable topics. However, what I find really weird is that they are all roughly the same size! Each topic catches around 5% of tokens and 5% of texts. In terms of the tokens, the smallest topic is 4.5% the largest 5.5%.
Can anybody suggest if this a 'normal' behaviour? This is the code I'm using:
ldafitted <- LDA(sentences.tm, k = K, method = "Gibbs", control = list(alpha = 0.1, # default is 50/k which would be 2.5. a lower alpha value places more weight on having each document composed of only a few dominant topics delta = 0.1, # default 0.1 is suggested in Griffiths and Steyvers (2004). estimate.beta = TRUE, verbose = 50, # print every 50th draw to screen seed = 5926696, save = 0, # can save model every xth iteration iter = 5000, burnin = 500, thin = 5000, # every thin iteration is returned for iter iterations. Standard is same as iter best = TRUE)) #only the best draw is returned
In short: My question is if there are circumstances under which it is reasonable that Latent Dirichlet allocation will cluster text in topics of equal size? Or is it something I should be worried if it happens?