topicmodels的LDA()函数中的其他种子词参数

Question

我正在寻找一个具有为R.中的topicmodels包指定种子字的Latent Dirichlet Allocation(LDA(的深度示例

基本函数采用以下形式：
LDA(x，k，method="Gibbs"，control=NULL，model=NULL，…(

文件只说明：

对于方法="Gibbs"，可以指定额外的参数种子字作为矩阵或类"simple_triplet_matrix"的对象；默认值为NULL。

有人能给我举一个完整的例子来说明它的外观和功能吗？

Answer 1

取自此答案：https://stats.stackexchange.com/questions/384183/seeded-lda-using-topicmodels-in-r

library("topicmodels")
data("AssociatedPress", package = "topicmodels")
## We fit 6 topics.
## We specify five seed words for five topics, the sixth topic has no
## seed words.
library("slam")
set.seed(123)
i <- rep(1:5, each = 5)
j <- sample(1:ncol(AssociatedPress), 25)
SeedWeight <- 500 - 0.1
deltaS <- simple_triplet_matrix(i, j, v = rep(SeedWeight, 25),
nrow = 6, ncol = ncol(AssociatedPress))
set.seed(1000)
ldaS <- LDA(AssociatedPress, k = 6, method = "Gibbs", seedwords = deltaS, 
control = list(alpha = 0.1, best = TRUE,
verbose = 500, burnin = 500, iter = 100, thin = 100, prefix = character()))
apply(deltaS, 1, function(x) which(x == SeedWeight))
apply(posterior(ldaS)$terms, 1, function(x) order(x, decreasing = TRUE)[1:5])