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我有一个数据集（Facebook 帖子）（通过 netvizz），我使用 R 中的 quanteda 包。这是我的 R 代码。

# Load the relevant dictionary (relevant for analysis)
liwcdict <- dictionary(file = "D:/LIWC2001_English.dic", format = "LIWC")

# Read File
# Facebooks posts could be generated by  FB Netvizz 
# https://apps.facebook.com/netvizz
# Load FB posts as .csv-file from .zip-file 
fbpost <- read.csv("D:/FB-com.csv", sep=";")

# Define the relevant column(s)
fb_test <-as.character(FB_com$comment_message) #one column with 2700 entries
# Define as corpus
fb_corp <-corpus(fb_test)
class(fb_corp)

# LIWC Application
fb_liwc<-dfm(fb_corp, dictionary=liwcdict)
View(fb_liwc)

一切正常，直到：

> fb_liwc<-dfm(fb_corp, dictionary=liwcdict)
Creating a dfm from a corpus ...
   ... indexing 2,760 documents
   ... tokenizing texts, found 77,923 total tokens
   ... cleaning the tokens, 1584 removed entirely
   ... applying a dictionary consisting of 68 key entries
Error in `dimnames<-.data.frame`(`*tmp*`, value = list(docs = c("text1",  : 
  invalid 'dimnames' given for data frame

你会如何解释错误信息？有解决问题的建议吗？

4

1 回答 1

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quanteda 0.7.2 版中存在一个错误，dfm()当其中一个文档不包含任何功能时，使用字典会导致失败。您的示例失败，因为在清理阶段，一些 Facebook 帖子“文档”最终通过清理步骤删除了所有功能。

这不仅在 0.8.0 中得到了修复，而且我们更改了字典的底层实现dfm()，从而显着提高了速度。（LIWC 仍然是一个庞大而复杂的字典，正则表达式仍然意味着它比简单的索引标记使用起来要慢得多。我们将进一步优化它。）

devtools::install_github("kbenoit/quanteda")
liwcdict <- dictionary(file = "LIWC2001_English.dic", format = "LIWC")
mydfm <- dfm(inaugTexts, dictionary = liwcdict)
## Creating a dfm from a character vector ...
##    ... indexing 57 documents
##    ... lowercasing
##    ... tokenizing
##    ... shaping tokens into data.table, found 134,024 total tokens
##    ... applying a dictionary consisting of 68 key entries
##    ... summing dictionary-matched features by document
##    ... indexing 68 feature types
##    ... building sparse matrix
##    ... created a 57 x 68 sparse dfm
##    ... complete. Elapsed time: 14.005 seconds.
topfeatures(mydfm, decreasing=FALSE)
## Fillers   Nonfl   Swear      TV  Eating   Sleep   Groom   Death  Sports  Sexual 
##       0       0       0      42      47      49      53      76      81     100

如果文档在标记化和清理后包含零特征，它也将起作用，这可能会破坏dfm您在 Facebook 文本中使用的旧版本。

mytexts <- inaugTexts
mytexts[3] <- ""
mydfm <- dfm(mytexts, dictionary = liwcdict, verbose = FALSE)
which(rowSums(mydfm)==0)
## 1797-Adams 
##          3
于 2015-07-01T07:19:28.260 回答