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这是结果错误，我可以说这是因为至少有一个文档没有某个术语，但我不明白为什么以及如何解决它。

prep_fun = function(x) {
  x %>% 
    str_to_lower                         %>%   #make text lower case
    str_replace_all("[^[:alpha:]]", " ") %>%   #remove non-alpha symbols - chao punctuation y #
    str_replace_all("\\s+", " ")         %>%   #collapse multiple spaces 
    str_replace_all("\\W*\\b\\w\\b\\W*", " ")  #Remuevo letras individuales
}
tok_fun <- function(x) {
  tokens <- word_tokenizer(x)
  textstem::lemmatize_words(tokens)
}
it_patentes <- itoken(data$Abstract, 
                      preprocessor = prep_fun, 
                      tokenizer = tok_fun, 
                      ids = data$id,
                      progressbar = F)
vocab <- create_vocabulary(it_patentes, ngram = c(ngram_min = 1L, ngram_max = 3L), 
                           stopwords = tm::stopwords("english"))
pruned_vocab <- prune_vocabulary(vocab, term_count_min =  max(vocab$term_count)*.01, 
                                 doc_proportion_min = 0.001)   
vectorizer <- vocab_vectorizer(pruned_vocab) 
dtm <- create_dtm(it_patentes, vectorizer,type = "dgTMatrix", progressbar = FALSE)   

> #Plot the metrics to get number of topics 
> t1 <- Sys.time()
> tunes <- FindTopicsNumber(
+   dtm = dtm,
+   topics = c(2:25),
+   metrics = c("Griffiths2004", "CaoJuan2009", "Arun2010"),
+   method = "Gibbs",
+   control = list(seed = 17),
+   mc.cores = 4L,
+   verbose = TRUE
+ )
fit models...Error in checkForRemoteErrors(val) : 
  4 nodes produced errors; first error: Each row of the input matrix needs to contain at least one non-zero entry
> print(difftime(Sys.time(), t1, units = 'sec'))
Time difference of 9.155343 secs
> FindTopicsNumber_plot(tunes)
Error in base::subset(values, select = 2:ncol(values)) : 
  object 'tunes' not found

尽管我知道 ldatuning 是为主题模型制作的，但我认为获得一个数字开始测试可能不会有很大的不同，是吗？

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1 回答 1

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ldatuningdtm需要不同格式的输入矩阵（来自topicmodels包的格式）。您需要将（来自 Matrix 包的稀疏矩阵）转换为可以理解dtm的格式ldatuning

于 2019-10-27T11:05:20.000 回答