Document-by-emoji feature matrix

emoji_dfm() turns a text column into a wide, model-ready table with one row per document and one column per emoji, weighted by raw counts, binary presence or tf-idf. All documents are kept, including those with no emoji (all-zero rows), so the result aligns row-for-row with the corpus and can be bound to outcome columns for tidymodels-style workflows.

Usage

emoji_dfm(data, text, doc_id = NULL, weighting = c("count", "binary", "tfidf"))

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.

doc_id

Optional unquoted column identifying documents; rows sharing a value are aggregated into one document. Default: each row is a document.

weighting

One of "count" (default), "binary" or "tfidf".

Value

A tibble with one row per document: .row_number (or the doc_id column) followed by one numeric column per emoji, ordered by descending total count across the corpus with ties broken by the glyph. That ordering is computed in the C locale, so the column order does not depend on the session's collation and is safe to index by position. Zero emoji in the corpus yields just the document column.

Details

By default every row of data is a document and the first output column, .row_number, is its position in data (matching emoji_extract_unnest()). Give doc_id to aggregate rows sharing an id into one document; the id column keeps its name, and documents appear in the order their id is first seen in data, never in the session's collation order. Emoji columns are named by the glyph itself, canonicalised through the package's codepoint key (so qualified and unqualified forms count as one feature), and ordered by descending total count (ties broken by glyph).

For weighting = "tfidf", the cell for emoji e in document d is count(d, e) * log(N / df(e)), where N is the number of documents and df(e) the number of documents containing e. An emoji that appears in every document therefore scores 0.

See also

emoji_frequency() for corpus totals; emoji_tokens() for the long form this widens.

Examples

df <- data.frame(text = c("\U0001f600\U0001f600 fun", "\U0001f621",
                          "no emoji"))
emoji_dfm(df, text)
#> # A tibble: 3 × 3
#>   .row_number  `😀`  `😡`
#>         <int> <int> <int>
#> 1           1     2     0
#> 2           2     0     1
#> 3           3     0     0
emoji_dfm(df, text, weighting = "binary")
#> # A tibble: 3 × 3
#>   .row_number  `😀`  `😡`
#>         <int> <dbl> <dbl>
#> 1           1     1     0
#> 2           2     0     1
#> 3           3     0     0
emoji_dfm(df, text, weighting = "tfidf")
#> # A tibble: 3 × 3
#>   .row_number  `😀`  `😡`
#>         <int> <dbl> <dbl>
#> 1           1  2.20  0   
#> 2           2  0     1.10
#> 3           3  0     0