topWords: Top Words per Topic
In tosca: Tools for Statistical Content Analysis

Description Usage Arguments Value Examples

Determines the top words per topic as top.topic.words do. In addition, it is possible to request the values that are taken for determining the top words per topic. Therefore, the function importance is used, which also can be called independently.

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topWords(topics, numWords = 1, byScore = TRUE, epsilon = 1e-05, values = FALSE)

importance(topics, epsilon = 1e-05)

`topics`	`named matrix`: The counts of vocabularies (column wise) in topics (row wise).
`numWords`	`integer(1)`: The number of requested top words per topic.
`byScore`	`logical(1)`: Should the values that are taken for determining the top words per topic be calculated by the function `importance` (`TRUE`) or should the absolute counts be considered (`FALSE`)?
`epsilon`	`numeric(1)`: Small number to add to logarithmic calculations to overcome the issue of determining `log(0)`.
`values`	`logical(1)`: Should the values that are taken for determining the top words per topic be returned?

Matrix of top words or, if value is TRUE a list of matrices with entries word and val.

texts <- list(
 A = "Give a Man a Fish, and You Feed Him for a Day.
      Teach a Man To Fish, and You Feed Him for a Lifetime",
 B = "So Long, and Thanks for All the Fish",
 C = "A very able manipulative mathematician, Fisher enjoys a real mastery
      in evaluating complicated multiple integrals.")

corpus <- textmeta(meta = data.frame(id = c("A", "B", "C", "D"),
  title = c("Fishing", "Don't panic!", "Sir Ronald", "Berlin"),
  date = c("1885-01-02", "1979-03-04", "1951-05-06", "1967-06-02"),
  additionalVariable = 1:4, stringsAsFactors = FALSE), text = texts)

corpus <- cleanTexts(corpus)
wordlist <- makeWordlist(corpus$text)
ldaPrep <- LDAprep(text = corpus$text, vocab = wordlist$words)

LDA <- LDAgen(documents = ldaPrep, K = 3L, vocab = wordlist$words, num.words = 3)
topWords(LDA$topics)

importance(LDA$topics)