R语言中Quanteda创建Corpus与DFM后文档数量减少的问题求助

Hey there, let's walk through the most likely reasons your corpus or DFM is missing documents, plus how to fix each issue:

1. Empty or Whitespace-Only Files Are Being Removed #

Quanteda automatically filters out documents that have no content (or only spaces/newlines) by default when creating a corpus. This is a sensible default, but it might catch you off guard if you have files that look "empty" but you intended to keep them.

Fixes: #

• When creating your corpus, disable the empty-document filter with the remove_empty parameter:

  my_corpus <- corpus(my_readtext_data, remove_empty = FALSE)
  

• After creating the corpus, identify which docs are empty to decide whether to keep or remove them:

  # Find docs with zero tokens
  empty_docs <- which(ntoken(my_corpus) == 0)
  # View their filenames (if using readtext)
  docvars(my_corpus, "doc_id")[empty_docs]
  

2. Files Failed to Read Properly #

If some files have encoding issues, are corrupted, or are in a format Quanteda can't parse, they might get skipped during the read process (often with a warning you might have missed!).

Fixes: #

• First, verify how many files you actually read vs. expected:

  # Check number of files in your directory
  length(list.files("your_file_directory", pattern = "*.txt")) # adjust pattern as needed
  # Check number of docs in your readtext object
  my_readtext <- readtext("your_file_directory/*.txt")
  nrow(my_readtext)
  

• If counts don't match, specify the correct file encoding (common issues with non-UTF-8 files):

  my_readtext <- readtext("your_file_directory/*.txt", encoding = "Latin-1") # or "UTF-16", etc.
  

• Keep an eye on the R console for warnings about unreadable files—they'll tell you exactly which ones failed.

3. DFM Construction Filtered Out Token-Less Documents #

Even if your corpus has all documents, when building a DFM, any document that ends up with zero tokens (after tokenization, removing stopwords, trimming rare terms, etc.) will be dropped by default.

Fixes: #

• Keep these empty rows in your DFM by setting remove_empty = FALSE in dfm():

  my_dfm <- dfm(my_corpus, remove_empty = FALSE)
  

• Pre-process your corpus to avoid empty docs first:

  # Remove stopwords but avoid stripping all content
  my_corpus_clean <- tokens(my_corpus) %>% 
    tokens_remove(stopwords("en")) %>% 
    tokens_remove(pattern = "^\\p{Punct}+$", valuetype = "regex") # keep meaningful terms
  # Check token counts again before building DFM
  ntoken(my_corpus_clean)
  

4. File Path/Pattern Matching Mistakes #

It's easy to accidentally mismatch the files you're trying to read—maybe your wildcard pattern is wrong, or you're not including subdirectories.

Fixes: #

• Double-check your file path and pattern:

  # List all files matching your pattern to confirm
  list.files("your_file_directory", pattern = "*.txt", full.names = TRUE)
  

• If you need to include files in subfolders, use recursive = TRUE:

  my_readtext <- readtext("your_file_directory/**/*.txt", recursive = TRUE)
  

内容的提问来源于stack exchange，提问作者Nicholas Bradley