A relatively slow version of the Rapid Automatic Keyword Extraction (RAKE)
algorithm. See Automatic keyword extraction from individual documents for
details on how RAKE works or read the "Getting started" vignette (
slowrake(txt, stop_words = smart_words, stop_pos = c("VB", "VBD", "VBG", "VBN", "VBP", "VBZ"), word_min_char = 3, stem = TRUE, phrase_delims = "[[:space:]]-[[:space:]]|[,.?():;\"!/]|]|\\[")
A character vector, where each element of the vector contains the text for one document.
A vector of stop words which will be removed from your
documents. The default value (
All words that have a part-of-speech (POS) that appears in
The minimum number of characters that a word must have
to remain in the corpus. Words with fewer than
Do you want to stem the words before running RAKE?
A regular expression containing the patterns that will be used as phrase delimiters.
An object of class
rakelist, which is just a list of data
frames (one data frame for each element of
txt). Each data frame
will have the following columns:
A keyword that was identified by RAKE.
The number of times the keyword appears in the document.
The keyword's score, as per the RAKE algorithm. Keywords with higher scores are considered to be higher quality than those with lower scores.
If you specified
stem = TRUE, you will get the
stemmed versions of the keywords in this column. When you choose stemming,
the keyword's score (
score) will be based off its stem,
but the reported number of times that the keyword appears (
will still be based off of the raw, unstemmed version of the keyword.
slowrake(txt = "some text that has great keywords")#> #> # A rakelist containing 1 data frames: #> $ :'data.frame': 2 obs. of 4 variables: #> ..$ keyword:"great keywords" ... #> ..$ freq :1 1 #> ..$ score :4 1 #> ..$ stem :"great keyword" ...slowrake(txt = dog_pubs$title[1:2], stem = FALSE)#> | | | 0% | |=================================== | 50% | |======================================================================| 100%#> #> # A rakelist containing 2 data frames: #> $ :'data.frame': 4 obs. of 3 variables: #> ..$ keyword:"assistance dogs" ... #> ..$ freq :1 1 ... #> ..$ score :4 4 ... #> $ :'data.frame': 6 obs. of 3 variables: #> ..$ keyword:"guide dog owners perspectives" ... #> ..$ freq :1 1 ... #> ..$ score :13 ...