Compute term frequencies from a vector of text
compute_term_frequency(
txt,
ignore_words = c("www.jstor.org", "www.arxiv.org", "arxiv.org", "provides", "https"),
stem = FALSE,
remove_punctuation = TRUE,
remove_stopwords = TRUE,
remove_numbers = TRUE,
to_lower = TRUE,
frequency = "term"
)Either a named numeric vector (frequency = "term"), or an object of class tm::DocumentTermMatrix (frequency = "document-term"), or or an object of class tm::TermDocumentMatrix (frequency = "term-document").
a vector of character strings.
a vector of words to be ignored when forming the corpus.
should words be stemmed using Porter's stemming algorithm? Default is FALSE. See tm::stemDocument().
should punctuation be removed when forming the corpus? Default is TRUE. See tm::removePunctuation().
should english stopwords be removed when forming the corpus? Default is TRUE. See tm::removeWords and tm::stopwords.
should numbers be removed when forming the corpus? Default is TRUE. See tm::removeNumbers.
should all terms be coerced to lower-case when forming the corpus? Default is TRUE.
the type of term frequencies to return. Options are "term" (default; a named vector of term frequencies), "document-term" (a document-term frequency matrix; see tm::TermDocumentMatrix()), "term-document" (a term-document frequency matrix; see tm::DocumentTermMatrix()).
The operations are taking place as follows: remove special
characters, covert to lower-case (depending on the values of
to_lower), remove numbers (depending on the value of
remove_numbers), remove stop words (depending on the value of
remove_stopwords), remove custom words (depending on the value of
ignore_words), remove punctuation (depending on the value of
remove_punctuation), clean up any leading or trailing whitespace,
and, finally stem words (depending on the value of stem).
If txt is a named vector then the names are used as document id's
when forming the corpus.
word_cloud()