Question

我正在使用'topicmodels'包在R中构建一些主题模型。在预处理和创建文档术语矩阵之后，我将应用以下LDA Gibbs模型。这可能是一个简单的答案，但我是R的新手，所以在这里。有没有办法可以将主题和术语列表及其概率导出到文本文件或Excel文件中？我可以在R中打印它们（如下所示），但不知道如何导出:(

这主要是因为我可以做一些可视化，我确信可以在Excel中完成，但就像我提到的我是新手并且没有太多可用于学习R中的可视化技术。希望这个有道理

k = 33
burnin = 1000
iter = 1000
keep = 50
seed = 2003


model_lda <- LDA(myDtm, k = k, method = "Gibbs",control = list(seed = seed, burnin =     burnin, iter = iter, keep = keep))
print(model_lda)
save(model_lda, file = "LDA_Output.RData")

topics(model_lda, 5)
terms(model_lda, 15)


 Topic 1   Topic 2    Topic 3       Topic 4   Topic 5    Topic 6    Topic 7 
[1,] "seat"    "dialogu"  "websit"      "census"  "northern" "growth"   "hse"   
[2,] "resum"   "church"   "partnership" "disabl"  "univers"  "adjust"   "legisl"
[3,] "suspend" "congreg"  "nesc"        "cso"     "peac"     "forecast" "die"   
[4,] "adjourn" "school"   "site"        "statist" "unemploy" "bernard"  "legal" 
[5,] "fisheri" "survivor" "nesf"        "survey"  "polic"    "burton"   "child"

Answer 1

首先，您可以使用permuteRanges读取数据，然后可以使用readr R包。例如：

tidytext

上述代码应分别在readr::write_csv(tidy(model_lda, "beta"), "beta.csv") readr::write_csv(tidy(model_lda, "gamma"), "gamma.csv")和beta中保存gamma矩阵和beta.csv矩阵。

您还可以在此处找到对我有用的章节：http://tidytextmining.com/topicmodeling.html

R - LDA主题模型输出数据

1 个答案: