Score the sentiment of the emoji in each row

emoji_sentiment() adds the mean emoji sentiment of each row, based on the Emoji Sentiment Ranking lexicon (see emoji_sentiment_lexicon). Scores range from -1 (negative) through 0 (neutral) to +1 (positive). Rows that contain no emoji, or whose emoji are absent from the lexicon, receive NA.

Usage

emoji_sentiment(data, text, lexicon = "novak2015", se = FALSE)

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

Lexicon to use. The default, "novak2015", uses the bundled emoji_sentiment_lexicon. A registered lexicon (see register_emoji_lexicon()) or a data frame can also be supplied; see emoji_score() for the generic scorer.

se

If TRUE, also return .emoji_sentiment_se, the standard error of the row's mean sentiment. Requires the bundled "novak2015" lexicon. Default FALSE.

Value

data, as a tibble, with added columns .emoji_n (the number of emoji in the row), .emoji_n_scored (the number of emoji that actually appear in the lexicon), and .emoji_sentiment (the mean sentiment of the scored emoji). With se = TRUE, also .emoji_sentiment_se.

Details

The lexicon covers about 19% of the distinct emoji tidyEmoji can detect, and nothing added to Unicode after 2015, so NA is a common and meaningful answer. .emoji_n_scored reports the shortfall per row: 0 means the row had emoji that the lexicon could not score, NA that it had none at all. See emoji_sentiment_lexicon for the figure and its denominator.

Detection is grapheme-aware. Some lexicon entries are stored as unqualified, text-presentation code points (notably the bare heart, U+2764, without the U+FE0F variation selector); those are not treated as emoji in your text, so they are neither counted nor scored. Supply the emoji-presentation (qualified) form and it resolves normally. See emoji_sentiment_lexicon for the full picture.

Uncertainty

A glyph annotated eight times should not carry the same authority as one annotated eight thousand times, and the bundled lexicon keeps the annotation counts that say which is which. With se = TRUE the result gains .emoji_sentiment_se, the standard error of the row's mean: each glyph's score has a binomial-style standard error computed from its own counts, and those are propagated to the mean assuming independent annotations (sqrt(sum(se^2)) / n_scored). It needs the annotation counts, so it is available for the bundled "novak2015" lexicon only. See emoji_ambiguity() for the same counts read as disagreement.

References

Kralj Novak P, Smailovic J, Sluban B, Mozetic I (2015) Sentiment of Emojis. PLoS ONE 10(12): e0144296. doi:10.1371/journal.pone.0144296

See also

emoji_sentiment_lexicon for the underlying scores; emoji_score() for scoring against any lexicon; emoji_emotion() for discrete emotions; emoji_ambiguity() for annotator disagreement.

Examples

df <- data.frame(text = c("love it \U0001f60d", "awful \U0001f621", "meh"))
emoji_sentiment(df, text)
#> # A tibble: 3 × 4
#>   text       .emoji_n .emoji_n_scored .emoji_sentiment
#>   <chr>         <int>           <int>            <dbl>
#> 1 love it 😍        1               1            0.678
#> 2 awful 😡          1               1           -0.173
#> 3 meh               0              NA           NA    
emoji_sentiment(df, text, se = TRUE)
#> # A tibble: 3 × 5
#>   text       .emoji_n .emoji_n_scored .emoji_sentiment .emoji_sentiment_se
#>   <chr>         <int>           <int>            <dbl>               <dbl>
#> 1 love it 😍        1               1            0.678             0.00711
#> 2 awful 😡          1               1           -0.173             0.0338 
#> 3 meh               0              NA           NA                NA