Text-emoji sentiment mismatch

emoji_incongruity() measures the signed gap between the sentiment a row's emoji carry and the sentiment of its text. It is the sarcasm-detection feature the NLP literature keeps rediscovering, and the mismatch variable the marketing literature calls (in)congruence.

Usage

emoji_incongruity(
  data,
  text,
  text_score,
  method = c("difference", "sign_flip"),
  scale,
  where = c("all", "final"),
  threshold = 1
)

Arguments

data

A data frame or tibble containing a text column.

text

The text column to scan, supplied unquoted.

text_score

Unquoted numeric column holding the text's own sentiment.

method

"difference" (default) for the continuous gap, or "sign_flip" for the categorical polarity-flip feature.

scale

How to make the two scores comparable: "rank", "zscore" or "none". Required – there is no sensible default.

where

"all" (default) scores every emoji in the row; "final" scores only the trailing run of emoji that ends the text.

threshold

For method = "difference", the absolute gap at or above which .emoji_incongruent is TRUE. Default 1, a full polarity swing on the rank scale.

Value

data, as a tibble, with added columns .emoji_n, .emoji_n_scored, .emoji_sentiment, .emoji_incongruity, .emoji_polarity_flip and .emoji_incongruent.

Details

.emoji_incongruity is emoji - text after scaling, so it is positive when the emoji is the more positive of the two. "sign_flip" is the categorical version most sarcasm papers use and is computed on the unscaled scores, where the sign means something.

A row with no scorable emoji gets NA, never 0: a neutral emoji and no emoji at all are different states, and collapsing them silently biases every downstream model. The same applies to a missing text_score.

With where = "final" only the run of emoji that ends the text is scored: both the illocutionary-force account of emoji and the P600 evidence on ironic emoji are specifically about sentence-final glyphs. A text whose emoji sit mid-sentence then has nothing eligible to score, so it gets NA and .emoji_n_scored = NA, while .emoji_n still counts every emoji in the row.

You supply the text score

tidyEmoji deliberately does not score text. text_score is a column you produce with tidytext and AFINN or Bing, sentimentr, vader, or a transformer – which keeps the method choice visible in your script instead of buried in this package, and keeps our dependency footprint where it is.

Because those methods live on wildly different scales (AFINN runs -5 to 5, VADER -1 to 1, a model's logits on nothing in particular), scale has no default: you have to say how the two sides were made comparable. "rank" maps both to percentiles on [-1, 1] and is the safest choice for cross-method comparison; "zscore" standardises both; "none" compares the raw numbers, which is only meaningful if your text score already lives on the emoji lexicon's -1 to 1 scale.

References

An emoji centric approach to sarcasm detection in online discourse. Scientific Reports (2025). The influence of emoji meaning multipleness on perceived online review helpfulness. *Journal of Business Research* (2022).

See also

emoji_congruence() for the same engine under the marketing framing; emoji_incongruity_profile() for which glyphs go against the grain; emoji_sentiment() for the emoji side on its own.

Examples

df <- data.frame(
  text = c("this is wonderful \U0001f621", "awful \U0001f621", "great \U0001f600"),
  score = c(0.9, -0.8, 0.7)
)
emoji_incongruity(df, text, score, scale = "none")
#> # A tibble: 3 × 8
#>   text        score .emoji_n .emoji_n_scored .emoji_sentiment .emoji_incongruity
#>   <chr>       <dbl>    <int>           <int>            <dbl>              <dbl>
#> 1 this is wo…   0.9        1               1           -0.173             -1.07 
#> 2 awful 😡     -0.8        1               1           -0.173              0.627
#> 3 great 😀      0.7        1               1            0.572             -0.128
#> # ℹ 2 more variables: .emoji_polarity_flip <lgl>, .emoji_incongruent <lgl>
emoji_incongruity(df, text, score, scale = "none", method = "sign_flip")
#> # A tibble: 3 × 8
#>   text        score .emoji_n .emoji_n_scored .emoji_sentiment .emoji_incongruity
#>   <chr>       <dbl>    <int>           <int>            <dbl>              <dbl>
#> 1 this is wo…   0.9        1               1           -0.173             -1.07 
#> 2 awful 😡     -0.8        1               1           -0.173              0.627
#> 3 great 😀      0.7        1               1            0.572             -0.128
#> # ℹ 2 more variables: .emoji_polarity_flip <lgl>, .emoji_incongruent <lgl>