r - LDA TopicModels 生成数字列表而不是术语

Question

请耐心等待，因为我对此非常陌生，并且正在为证书类(class)的类(class)工作。

我有 .csv 数据集，我通过从 Pubmed 和 Embase 数据库检索文献计量记录获得。有 1034 行。有几列，但是，我试图仅从一列创建主题模型，摘要列和一些记录没有摘要​​。我已经做了一些处理(删除停用词、标点符号等)，并且能够将出现超过 200 次的单词绘制成条形图，并按等级创建一个常用词列表，还可以运行与选定单词的单词关联。因此，似乎 r 在 Abstract 字段中看到了单词本身。当我尝试使用 topicmodels 包创建主题模型时，我的问题就出现了。这是我正在使用的代码。

#including 1st 3 lines for reference
options(header = FALSE, stringsAsFactors = FALSE, FileEncoding = 
"latin1")
records <- read.csv("Combined.csv")
AbstractCorpus <- Corpus(VectorSource(records$Abstract))

AbstractTDM <- TermDocumentMatrix(AbstractCorpus)
library(topicmodels)
library(lda)
lda <- LDA(AbstractTDM, k = 8)
(term <- terms(lda, 6))
term <- (apply(term, MARGIN = 2, paste, collapse = ","))

但是，我得到的主题输出如下。

Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 Topic 6 Topic 7 Topic 8

[1,] "499"   "733"   "390"   "833"   "17"    "413"   "719"   "392"  
[2,] "484"   "655"   "808"   "412"   "550"   "881"   "721"   "61"   
[3,] "857"   "299"   "878"   "909"   "15"    "258"   "47"    "164"  
[4,] "491"   "672"   "313"   "1028"  "126"   "55"    "375"   "987"  
[5,] "734"   "430"   "405"   "102"   "13"    "193"   "83"    "588"  
[6,] "403"   "52"    "489"   "10"    "598"   "52"    "933"   "980"  

为什么我在这里看不到文字而不是数字？

此外，我基本上从主题模型上的 r PDF 中获取的以下代码确实为我生成了值，但主题仍然是数字而不是单词，这对我来说毫无意义。

#using information from topicmodels paper
library(tm)
library(topicmodels)
library(lda)
AbstractTM <- list(VEM = LDA(AbstractTDM, k = 10, control = list(seed =    
505)), VEM_fixed = LDA(AbstractTDM, k = 10, control = list(estimate.alpha 
= FALSE, seed = 505)), Gibbs = LDA(AbstractTDM, k = 10, method = "Gibbs", 
Control = list(seed = 505, burnin = 100, thin = 10, iter = 100)), CTM = 
CTM(AbstractTDM, k = 10, control = list(seed = 505, var = list(tol = 
10^-4), em = list(tol = 10^-3))))
#To compare the fitted models we first investigate the α values of the    
models fitted with VEM and α estimated and with VEM and α fixed 

sapply(AbstractTM[1:2], slot, "alpha")

#Find entropy 
sapply(AbstractTM, function(x)mean(apply(posterior(x)$topics, 1, 
function(z) - sum(z * log(z)))))

#Find estimated topics and terms
Topic <- topics(AbstractTM[["VEM"]], 1)
Topic
#find 5 most frequent terms for each topic
Terms <- terms(AbstractTM[["VEM"]], 5)
Terms[,1:5]

关于问题可能是什么的任何想法？

Answer 1

阅读 topicmodels 文档，似乎 LDA()函数需要一个 DocumentTermMatrix ，不是 TermDocumentMatrix .尝试使用 DocumentTermMatrix(AbstractCorpus) 创建前者看看这是否有效。