Question

我正在寻找一个深入的潜在Dirichlet分配（LDA）示例，其中为R中的topicmodels包指定了种子词。

基本功能采用以下形式：
LDA（x，k，method =“ Gibbs”，控制= NULL，模型= NULL，...）

文档仅说明：

对于method =“ Gibbs”，可以指定其他参数seedwords 作为矩阵或“ simple_triplet_matrix”类的对象；默认值是NULL。

谁能指出一个完整的示例，说明其外观和功能？

Answer 1

从此答案中得出： 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])

LDA（）函数中来自topicmodels的其他seedwords参数

1 个答案: