Interpretation risk per row

emoji_risk() scores how likely each row's emoji are to be misread, using the annotation-disagreement statistics of emoji_ambiguity(). It is the content-QA counterpart of emoji_sentiment(): a row can carry a confident positive score built entirely out of glyphs its annotators fought over.

Usage

emoji_risk(data, text, measure = "entropy", threshold = NULL)

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.

measure

Ambiguity statistic to use; see emoji_ambiguity().

threshold

Value at or above which a glyph counts as ambiguous. NULL (default) uses the lexicon's upper quartile of measure.

Value

data, as a tibble, with added columns .emoji_n, .emoji_n_scored, .emoji_ambiguity_mean, .emoji_ambiguity_max and .emoji_n_ambiguous. Rows with no emoji get NA throughout. A row that has emoji the lexicon cannot score gets .emoji_n_scored = 0, .emoji_n_ambiguous = 0 and NA for the two averages – there is nothing to average, but the count of ambiguous glyphs found is genuinely zero.

Details

threshold decides what counts as an ambiguous glyph for .emoji_n_ambiguous. The default, NULL, uses the upper quartile of the chosen measure across the whole lexicon, i.e. "in the most-disputed quarter of all emoji". Supply your own number to make the cut-off explicit in your script.

Emoji absent from the lexicon cannot be scored and are excluded from the means; .emoji_n and .emoji_n_scored together show how much of the row was actually measured.

See also

emoji_ambiguity(), emoji_flag_ambiguous().

Examples

df <- data.frame(text = c("thanks \U0001f643", "great \U0001f600", "plain"))
emoji_risk(df, text)
#> # A tibble: 3 × 6
#>   text      .emoji_n .emoji_n_scored .emoji_ambiguity_mean .emoji_ambiguity_max
#>   <chr>        <int>           <int>                 <dbl>                <dbl>
#> 1 thanks 🙃        1               0                NA                   NA    
#> 2 great 😀         1               1                 0.835                0.835
#> 3 plain            0              NA                NA                   NA    
#> # ℹ 1 more variable: .emoji_n_ambiguous <int>