Emoji emotion profiles (the 8 Plutchik emotions)

emoji_emotion() scores each row's emoji across the eight Plutchik emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, trust) using the bundled EmoTag1200 lexicon (Shoeb & de Melo, 2020). Scores each range from 0 to 1 and are averaged over the emoji in the row that appear in the lexicon.

Usage

emoji_emotion(data, text, lexicon = "emotag1200", long = 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. Either a string naming a bundled lexicon ("emotag1200", the default), the name of a registered lexicon (see register_emoji_lexicon()), or a data frame. A custom lexicon must have an emoji column and one column per emotion (any subset of the eight Plutchik emotions); it is joined through the same codepoint-normalised key as the bundled one.
long: If TRUE, return one row per (row, emotion) in long form with columns .emoji_emotion (the emotion name) and .emoji_score (its mean). Default FALSE adds eight .emoji_<emotion> columns plus .emoji_n and .emoji_n_scored.

Value

data, as a tibble. With long = FALSE (the default), eight emotion columns – .emoji_anger, .emoji_anticipation, .emoji_disgust, .emoji_fear, .emoji_joy, .emoji_sadness, .emoji_surprise, .emoji_trust – plus .emoji_n and .emoji_n_scored, one row per input row. With long = TRUE, one row per input row per emotion, carrying .emoji_emotion and .emoji_score in place of the eight columns and of the two counts – the long form returns neither .emoji_n nor .emoji_n_scored. Rows without emoji, or whose emoji are absent from the lexicon, receive NA scores.

.emoji_n_scored is what tells those two apart, as in emoji_sentiment(): 0 means the row had emoji the lexicon could not score, NA that it had no emoji to score. Since the long form omits it, read the counts from a long = FALSE call on the same data (the rows are in the same order) when the distinction matters – on a 150-glyph lexicon it usually does.

Details

The lexicon is 150 glyphs, about 4% of the distinct emoji tidyEmoji can detect, so a row of post-2018 emoji will score NA and still be a row full of emoji. Read .emoji_n_scored alongside .emoji_n before concluding a corpus carries no emotion; see emoji_emotion_lexicon for the figure and its denominator.

References

Shoeb AAM, de Melo G (2020). EmoTag1200: Understanding the Association between Emojis and Emotions. EMNLP 2020. https://aclanthology.org/2020.emnlp-main.720/. Data released under the MIT licence.

Examples

df <- data.frame(text = c("love it \U0001f60d", "scary \U0001f628", "meh"))
emoji_emotion(df, text)
#> # A tibble: 3 × 11
#>   text    .emoji_anger .emoji_anticipation .emoji_disgust .emoji_fear .emoji_joy
#>   <chr>          <dbl>               <dbl>          <dbl>       <dbl>      <dbl>
#> 1 love i…         0                   0.31           0           0          0.83
#> 2 scary …         0.17                0.39           0.33        0.97       0   
#> 3 meh            NA                  NA             NA          NA         NA   
#> # ℹ 5 more variables: .emoji_sadness <dbl>, .emoji_surprise <dbl>,
#> #   .emoji_trust <dbl>, .emoji_n <int>, .emoji_n_scored <int>
emoji_emotion(df, text, long = TRUE)
#> # A tibble: 24 × 3
#>    text       .emoji_emotion .emoji_score
#>    <chr>      <chr>                 <dbl>
#>  1 love it 😍 anger                  0   
#>  2 love it 😍 anticipation           0.31
#>  3 love it 😍 disgust                0   
#>  4 love it 😍 fear                   0   
#>  5 love it 😍 joy                    0.83
#>  6 love it 😍 sadness                0   
#>  7 love it 😍 surprise               0.5 
#>  8 love it 😍 trust                  0.5 
#>  9 scary 😨   anger                  0.17
#> 10 scary 😨   anticipation           0.39
#> # ℹ 14 more rows