Which words keep company with which emoji

emoji_collocations() counts the words that appear near each emoji across a corpus and scores the association with pointwise mutual information. It is the corpus-derived alternative to importing a fixed sense inventory: the senses come from your texts, so they cannot be stale and carry no licence baggage.

Usage

emoji_collocations(
  data,
  text,
  window = 5,
  min_n = 3,
  measure = c("pmi", "count")
)

Arguments

data

A data frame or tibble containing a text column. Grouped data frames are accepted. The verbs that work a row at a time (adding columns, or keeping and expanding rows) carry the grouping through to their result, as dplyr::mutate() and dplyr::filter() do. The verbs that pool across rows – the counts, the co-occurrence edge lists, the time series – warn that they ignore the grouping and return one corpus-wide answer.

text

The text column to scan, supplied unquoted. Any atomic column is accepted and read as character, so a factor works and a numeric, Date or logical one simply contains no emoji. A list column – or a data-frame column – is refused rather than coerced, because coercing one deparses it and the emoji found would be in the code rather than in your data. What counts as an emoji is the same in every verb; see the Detection section of tidyEmoji for the one case that surprises people, code points that are emoji only when they carry U+FE0F.

window

Context window on each side, in words. Default 5.

min_n

Minimum number of co-occurrences for a pair to be reported. Default 3.

measure

Sort order: "pmi" (default) or "count". Both columns are always returned.

Value

A tibble with columns emoji, word, n (co-occurrences) and pmi, shaped like widyr::pairwise_count() output so it drops into existing tidytext workflows. Rows are sorted by measure descending, then by the other of the two descending, then by the glyph and the word, so the order is fully determined. Ties in pmi are common: every pair seen the same number of times with the same marginals scores alike.

Details

Each emoji occurrence contributes its context window (see emoji_context()). A word is counted once per occurrence however often it repeats inside that window. Words are lower-cased and stripped of leading and trailing characters that are not letters, digits or combining marks, and a token left holding no letter and no digit at all is not a word. All three rules read Unicode's own tables rather than the session's locale, so a corpus in any script gives the same answer wherever it is run. No stopword list is applied, because which stopwords are right is a decision for your analysis, not for this package – filter the result with tidytext's stop_words if you want one.

PMI is log(n(e, w) * N / (n(e) * n(w))), with N the total number of emoji-word co-occurrence events. Marginals are computed over all co-occurrences before min_n filters the rows, so a rare pairing is scored against the full corpus rather than against the surviving subset.

Glyphs are canonicalised through the package's codepoint key, so qualified and unqualified forms of the same emoji share one row.

On a script without spaces between words this verb cannot find a collocation at all, and it will not say so. Its tokens are the whitespace-delimited ones of emoji_context(), so a Chinese, Japanese, Thai, Khmer, Lao or Burmese clause is one token: three rows of Chinese reading "the weather is good today", "my mood is good today" and "going to Beijing tomorrow" yield three tokens, each a whole clause, each with n = 1, and nothing clears min_n. The same three sentences in English yield good, is and today at n = 2. Segment the column before it reaches this verb (see emoji_context()); min_n cannot rescue a vocabulary in which every type occurs once.

See also

emoji_context() for the occurrence-level windows this aggregates.

Examples

df <- data.frame(text = c("cold coffee \U0001f622",
                          "coffee again \U0001f622",
                          "warm tea \U0001f60a"))
emoji_collocations(df, text, min_n = 1)
#> # A tibble: 5 × 4
#>   emoji word       n   pmi
#>   <chr> <chr>  <int> <dbl>
#> 1 😊    tea        1 1.10 
#> 2 😊    warm       1 1.10 
#> 3 😢    coffee     2 0.405
#> 4 😢    again      1 0.405
#> 5 😢    cold       1 0.405