R：将 Tibbles 转换为术语文档矩阵

Question

我正在使用 R 编程语言。我学习了如何从互联网上获取 pdf 文件并将它们加载到 R 中。例如，下面我将莎士比亚的 3 部不同的书籍加载到 R 中：

library(pdftools)
library(tidytext)
library(textrank)
library(tm)

#1st document
url <- "https://shakespeare.folger.edu/downloads/pdf/hamlet_PDF_FolgerShakespeare.pdf"

article <- pdf_text(url)
article_sentences <- tibble(text = article) %>%
  unnest_tokens(sentence, text, token = "sentences") %>%
  mutate(sentence_id = row_number()) %>%
  select(sentence_id, sentence)


article_words <- article_sentences %>%
  unnest_tokens(word, sentence)


article_words_1 <- article_words %>%
  anti_join(stop_words, by = "word")

#2nd document
url <- "https://shakespeare.folger.edu/downloads/pdf/macbeth_PDF_FolgerShakespeare.pdf"

article <- pdf_text(url)
article_sentences <- tibble(text = article) %>%
  unnest_tokens(sentence, text, token = "sentences") %>%
  mutate(sentence_id = row_number()) %>%
  select(sentence_id, sentence)


article_words <- article_sentences %>%
  unnest_tokens(word, sentence)


article_words_2<- article_words %>%
  anti_join(stop_words, by = "word")


#3rd document
url <- "https://shakespeare.folger.edu/downloads/pdf/othello_PDF_FolgerShakespeare.pdf"

article <- pdf_text(url)
article_sentences <- tibble(text = article) %>%
  unnest_tokens(sentence, text, token = "sentences") %>%
  mutate(sentence_id = row_number()) %>%
  select(sentence_id, sentence)


article_words <- article_sentences %>%
  unnest_tokens(word, sentence)


article_words_3 <- article_words %>%
  anti_join(stop_words, by = "word")

这些文件中的每一个（例如 article_words_1）现在都是一个“tibble”文件。从这里开始，我想将这些转换为“文档术语矩阵”，以便我可以对这些执行文本挖掘和 NLP：

#convert to document term matrix
myCorpus <- Corpus(VectorSource(article_words_1, article_words_2, article_words_3))
tdm <- TermDocumentMatrix(myCorpus)
inspect(tdm)

但这似乎会导致错误：

Error in VectorSource(article_words_1, article_words_2, article_words_3) : 
  unused arguments (article_words_2, article_words_3)

谁能告诉我我做错了什么？

谢谢

Answer 1

正如错误消息所暗示的，VectorSource 只需要 1 个参数。您可以将数据集rbind 一起传递给VectorSource 函数。

library(tm)

tdm <- TermDocumentMatrix(Corpus(VectorSource(rbind(article_words_1, article_words_2, article_words_3))))
inspect(tdm)

#<<TermDocumentMatrix (terms: 14952, documents: 2)>>
#Non-/sparse entries: 14952/14952
#Sparsity           : 50%
#Maximal term length: 25
#Weighting          : term frequency (tf)
#Sample             :
#            Docs
#Terms        1     2
#  "act",     0   397
#  "cassio",  0   258
#  "ftln",    0 10303
#  "hamlet",  0   617
#  "iago",    0   371
#  "lord",    0   355
#  "macbeth", 0   386
#  "othello", 0   462
#  "sc",      0   337
#  "thou",    0   346