Discover, Count, Categorise, Score, Translate and Relate Emoji in Text

A tidy toolkit for working with the emoji in any text column, such as social-media posts, product reviews, chat logs or survey responses. Unicode is awkward to handle and not every code point is an emoji, which makes emoji statistics fiddly to obtain. 'tidyEmoji' extracts, counts, categorises, sentiment-scores and emotion-scores emoji, converts them to and from text (for accessibility and NLP preprocessing), searches the emoji catalogue, maps emoji co-occurrence and sequences (graph-ready edge lists and n-grams), measures where and how densely emoji are used, and builds document-by-emoji feature tables for machine learning, with grapheme-aware detection (so skin-tone and multi-person sequences stay intact), returning tidy data frames that slot straight into a 'tidyverse' workflow. It also quantifies how much annotators disagreed about an emoji (interpretation risk), extracts the words around each emoji, tracks emoji use over time, measures text-emoji sentiment mismatch, and applies explicit emoji-preprocessing policies for language-model pipelines. Its emoji catalogue is the Unicode emoji test data, bundled and current to Emoji 18.0, so it can also say what a glyph is, map flag emoji to ISO 3166 codes, report how much of a corpus each lexicon can score, and read reaction tallies as well as text. The bundled emoji sentiment lexicon is from the Emoji Sentiment Ranking of Kralj Novak et al. (2015) , released under CC BY-SA 4.0; the emotion lexicon is from EmoTag1200 of Shoeb & de Melo (2020) < https://aclanthology.org/2020.emnlp-main.720/>, released under the MIT licence.

tidyEmoji

Count, score, translate and relate the emoji in any text column, and get tidy data frames back.

library(tidyEmoji)

reviews <- data.frame(text = c("Best purchase ever \U0001f600\U0001f60d",
                               "It broke after a day \U0001f621",
                               "Does the job.",
                               "Shipped to \U0001f1ef\U0001f1f5 in two days \U0001f680"))

emoji_frequency(reviews, text)
#> # A tibble: 5 × 5
#>   emoji name                         shortcode  group                 n
#>   <chr> <chr>                        <chr>      <chr>             <int>
#> 1 🇯🇵    flag: Japan                  jp         Flags                 1
#> 2 😀    grinning face                grinning   Smileys & Emotion     1
#> 3 😍    smiling face with heart-eyes heart_eyes Smileys & Emotion     1
#> 4 😡    enraged face                 rage       Smileys & Emotion     1
#> 5 🚀    rocket                       rocket     Travel & Places       1
emoji_sentiment(reviews, text)
#> # A tibble: 4 × 4
#>   text                         .emoji_n .emoji_n_scored .emoji_sentiment
#>   <chr>                           <int>           <int>            <dbl>
#> 1 Best purchase ever 😀😍             2               2            0.625
#> 2 It broke after a day 😡             1               1           -0.173
#> 3 Does the job.                       0              NA           NA    
#> 4 Shipped to 🇯🇵 in two days 🚀        2               1            0.525

How much of that could the lexicon score? One more call says so:

emoji_coverage(reviews, text)[, c("lexicon", "occurrence_coverage",
                                  "n_rows_unscored")]
#> # A tibble: 2 × 3
#>   lexicon    occurrence_coverage n_rows_unscored
#>   <chr>                    <dbl>           <int>
#> 1 novak2015                  0.8               0
#> 2 emotag1200                 0.6               1

🚀 Get started

Install from CRAN: install.packages("tidyEmoji")
Load it: library(tidyEmoji)
Hand any verb a data frame and its text column: emoji_summary(df, text)
Take the tour: vignette("introduction", package = "tidyEmoji")

🧰 Pick a verb by question

Question	Verbs
How much emoji is here, and which?	`emoji_summary()`, `emoji_frequency()`, `emoji_tokens()`
What does it say?	`emoji_sentiment()`, `emoji_emotion()`, `emoji_coverage()`
Will readers agree on it?	`emoji_ambiguity()`, `emoji_risk()`
What is this glyph, exactly?	`emoji_properties()`, `emoji_identical()`, `emoji_find()`
What goes with what?	`emoji_pairs()`, `emoji_collocations()`, `emoji_ngrams()`
When, and how new?	`emoji_trend()`, `emoji_adoption_lag()`
Which countries do the flags name?	`emoji_country()`, `emoji_flag()`
Reaction counts rather than text?	`emoji_from_counts()`
Sending text to a language model?	`emoji_sanitize()`, `emoji_token_cost()`

💡 Tips

Count bare text-style emoji such as the plain heart with presentation = "any", which every counting and scoring verb takes.
Report emoji_coverage() next to any lexicon score: it says how much of the corpus the score covers.
Paste emoji_provenance() into a methods section: it records the Unicode catalogue (Emoji 18.0, bundled) and the lexicons behind a result.

📚 Learn more

Three articles ship with the package: vignette("introduction") for a full analysis of a real corpus, vignette("reversible-preprocessing") for emoji in language-model pipelines, and vignette("emoji-networks") for co-occurrence networks. All three are on the package website.