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Score emoji in a text column against any lexicon

Source: R/emoji-lexicon-api.R
emoji_score.Rd

emoji_score() is the generic scorer that the friendly verbs (emoji_sentiment(), emoji_emotion()) sit on top of. It joins each row's emoji to lexicon through emoji_key() and returns the per-row mean of the score column, plus the number of emoji scored. Bring your own lexicon, or name a bundled / registered one.

Usage

emoji_score(data, text, lexicon = "novak2015", by = "emoji", score = 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.

lexicon

Either a string naming a bundled or registered lexicon, or a data frame. For data frames, by names the glyph column and score the score column. Defaults to "novak2015", matching emoji_sentiment().

Two requirements on a data frame, both refused rather than worked around. The score column must be numeric or logical: as text every score comes back NA while the emoji still counts as scored, which contradicts .emoji_n_scored below. And no two rows may give one emoji different scores – spellings differing only by a variation selector share a single code-point key, so a table listing both U+2764 and U+2764 U+FE0F has one emoji twice. Identical scores are fine and collapse silently; when they differ, the row order would be choosing the answer.

A third value is neither refused nor used: an infinite score warns and is treated as missing, so the emoji carrying it counts as unscored. It is the same rule emoji_incongruity() applies to a non-finite text_score, and the same one NA and NaN already got. Left alone, one Inf makes every row that meets it infinite while .emoji_n_scored still reports the row as scored. This applies to an emotion lexicon's dimensions too, and those must be numeric for the reason the score column must be.

by

Glyph column name when lexicon is a data frame, as a single string. Default "emoji". Ignored when lexicon names a bundled or registered lexicon, which carries its own key.

score

Score column name when lexicon is a data frame. If NULL, "sentiment_score" then "score" are tried. Ignored, like by, when lexicon is a name rather than a table.

Value

data, as a tibble, with .emoji_n (total emoji), .emoji_n_scored (emoji found in the lexicon) and .emoji_score (per-row mean) added, in that order – the same order emoji_sentiment(), emoji_emotion(), emoji_risk(), emoji_incongruity() and emoji_faceness() use.

For the multi-dimensional "emotag1200" lexicon the score is the mean over its eight emotion dimensions; use emoji_emotion() for the per-emotion profile. Note the scale changes with the lexicon. "novak2015" is a signed valence on [-1, 1], where the sign is the direction of sentiment. The "emotag1200" mean is an intensity on [0, 1]: its eight dimensions are each non-negative and four of them (anger, disgust, fear, sadness) are negatively valenced, so a maximally angry emoji and a maximally joyful one score alike and neither is negative. The two are not comparable and must not be pooled.

That averaging is specific to the bundled lexicon. A registered or inline lexicon carrying emotion columns has no score column, so emoji_score() cannot collapse it and says so: pass it to emoji_emotion() instead, or name one dimension with score = "joy" to score on that alone.

.emoji_n_scored distinguishes the two ways a score can be missing, as in emoji_sentiment(): 0 means the row had emoji that the lexicon could not score, NA that it had no emoji to score. .emoji_n counts every emoji either way.

See also

emoji_lexicons(), register_emoji_lexicon().

Examples

df <- data.frame(text = c("love \U0001f60d", "angry \U0001f621", "meh"))
emoji_score(df, text, lexicon = "novak2015")
#> # A tibble: 3 × 4
#>   text     .emoji_n .emoji_n_scored .emoji_score
#>   <chr>       <int>           <int>        <dbl>
#> 1 love 😍         1               1        0.678
#> 2 angry 😡        1               1       -0.173
#> 3 meh             0              NA       NA    

# a bring-your-own lexicon
own <- data.frame(emoji = c("\U0001f600", "\U0001f621"),
                  score = c(0.9, -0.8))
emoji_score(df, text, lexicon = own)
#> # A tibble: 3 × 4
#>   text     .emoji_n .emoji_n_scored .emoji_score
#>   <chr>       <int>           <int>        <dbl>
#> 1 love 😍         1               0         NA  
#> 2 angry 😡        1               1         -0.8
#> 3 meh             0              NA         NA