tidyEmoji helps you discover, count, categorise, sentiment-score, score emotions, translate and relate the emoji in any text column — social-media posts, product reviews, chat logs, survey responses, support tickets — from summary counts through emotion profiles, co-occurrence networks, structural metrics and model-ready feature tables, always as tidy data frames that drop straight into a tidyverse workflow.
Unicode is awkward to work with and not every code point is an emoji, which makes emoji statistics fiddly. tidyEmoji takes care of that, including grapheme-aware detection so skin-tone modifiers (👍🏽) and multi-person sequences (👨👩👧👦) are treated as a single emoji rather than being split apart.
Installation
install.packages("tidyEmoji")
# development version
# install.packages("devtools")
devtools::install_github("PursuitOfDataScience/tidyEmoji")Usage
library(tidyEmoji)
library(dplyr)
reviews <- data.frame(text = c("Best purchase ever \U0001f600\U0001f60d",
"It broke after a day \U0001f621",
"Does the job.",
"Wearing my mask \U0001f637\U0001f637",
"Shipped fast \U0001f3c1\U0001f600"))How much emoji is in the data?
reviews %>% emoji_summary(text) # entries with emoji vs. total
#> # A tibble: 1 × 2
#> n_with_emoji n_total
#> <int> <int>
#> 1 4 5
reviews %>% emoji_filter(text) # keep only the rows that have emoji
#> # A tibble: 4 × 1
#> text
#> <chr>
#> 1 Best purchase ever 😀😍
#> 2 It broke after a day 😡
#> 3 Wearing my mask 😷😷
#> 4 Shipped fast 🏁😀Which emoji are most common?
reviews %>% emoji_frequency(text) # every emoji, with name + category
#> # A tibble: 5 × 5
#> emoji name shortcode group n
#> <chr> <chr> <chr> <chr> <int>
#> 1 😀 grinning face grinning Smileys & Emotion 2
#> 2 😷 face with medical mask mask Smileys & Emotion 2
#> 3 🏁 chequered flag checkered_flag Flags 1
#> 4 😍 smiling face with heart-eyes heart_eyes Smileys & Emotion 1
#> 5 😡 enraged face rage Smileys & Emotion 1
reviews %>% top_n_emojis(text, n = 3) # just the most frequent
#> # A tibble: 3 × 4
#> emoji_name unicode emoji_category n
#> <chr> <chr> <chr> <int>
#> 1 grinning 😀 Smileys & Emotion 2
#> 2 mask 😷 Smileys & Emotion 2
#> 3 checkered_flag 🏁 Flags 1Pull the emoji out
emoji_tokens() gives one tidy row per emoji occurrence, with its name, category and sentiment — ready to count, join or plot.
reviews %>% emoji_tokens(text)
#> # A tibble: 7 × 5
#> text .emoji .emoji_name .emoji_category .emoji_sentiment
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 Best purchase ever 😀😍 😀 grinning face Smileys & Emot… 0.572
#> 2 Best purchase ever 😀😍 😍 smiling face … Smileys & Emot… 0.678
#> 3 It broke after a day 😡 😡 enraged face Smileys & Emot… -0.173
#> 4 Wearing my mask 😷😷 😷 face with med… Smileys & Emot… -0.171
#> 5 Wearing my mask 😷😷 😷 face with med… Smileys & Emot… -0.171
#> 6 Shipped fast 🏁😀 🏁 chequered flag Flags 0.571
#> 7 Shipped fast 🏁😀 😀 grinning face Smileys & Emot… 0.572Categorise and score sentiment
reviews %>% emoji_categorize(text) # which Unicode categories each row spans
#> # A tibble: 4 × 2
#> text .emoji_category
#> <chr> <chr>
#> 1 Best purchase ever 😀😍 Smileys & Emotion
#> 2 It broke after a day 😡 Smileys & Emotion
#> 3 Wearing my mask 😷😷 Smileys & Emotion
#> 4 Shipped fast 🏁😀 Flags|Smileys & Emotion
reviews %>% emoji_sentiment(text) # mean emoji sentiment per row (-1 to +1)
#> # A tibble: 5 × 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 Wearing my mask 😷😷 2 2 -0.171
#> 5 Shipped fast 🏁😀 2 2 0.572emoji_sentiment() uses the bundled Emoji Sentiment Ranking lexicon (Kralj Novak et al., 2015). See the package vignette for a fuller tour.
Score emoji emotions
emoji_emotion() scores each row’s emoji across the eight Plutchik emotions using the bundled EmoTag1200 lexicon (Shoeb & de Melo, 2020).
reviews %>% emoji_emotion(text) %>% select(text, .emoji_joy, .emoji_trust,
.emoji_anger, .emoji_n)
#> # A tibble: 5 × 5
#> text .emoji_joy .emoji_trust .emoji_anger .emoji_n
#> <chr> <dbl> <dbl> <dbl> <int>
#> 1 Best purchase ever 😀😍 0.76 0.375 0.03 2
#> 2 It broke after a day 😡 0 0.06 1 1
#> 3 Does the job. NA NA NA 0
#> 4 Wearing my mask 😷😷 0 0.11 0.03 2
#> 5 Shipped fast 🏁😀 0.69 0.25 0.06 2
reviews %>% emoji_emotion_label(text) # the dominant emotion per row
#> # A tibble: 5 × 4
#> text .emoji_n .emoji_n_scored .emoji_emotion
#> <chr> <int> <int> <chr>
#> 1 Best purchase ever 😀😍 2 2 joy
#> 2 It broke after a day 😡 1 1 anger
#> 3 Does the job. 0 NA <NA>
#> 4 Wearing my mask 😷😷 2 2 disgust
#> 5 Shipped fast 🏁😀 2 1 joyRelate emoji to each other
emoji_pairs() returns a graph-ready edge list of the emoji that appear in the same entry (widyr-style item1/item2/n — pipe it into igraph, tidygraph or ggraph), and emoji_ngrams() captures consecutive sequences.
reviews %>% emoji_pairs(text)
#> # A tibble: 2 × 3
#> item1 item2 n
#> <chr> <chr> <int>
#> 1 🏁 😀 1
#> 2 😀 😍 1There are structural metrics too — emoji_position() (emoji sit at the end of messages), emoji_density() and emoji_ratio() (emoji-only detection) — and emoji_dfm() builds a document-by-emoji count/binary/tf-idf table for modelling.
Bring your own lexicon
The scoring machinery is pluggable: emoji_lexicons() lists what’s available, register_emoji_lexicon() adds your own, and emoji_score() scores a text column against any of them — always joining through a codepoint-normalised key, so qualified and unqualified emoji forms both match.
my_lexicon <- data.frame(emoji = c("\U0001f600", "\U0001f621"),
score = c(1, -1))
reviews %>% emoji_score(text, lexicon = my_lexicon)
#> # A tibble: 5 × 4
#> text .emoji_score .emoji_n_scored .emoji_n
#> <chr> <dbl> <int> <int>
#> 1 Best purchase ever 😀😍 1 1 2
#> 2 It broke after a day 😡 -1 1 1
#> 3 Does the job. NA NA 0
#> 4 Wearing my mask 😷😷 NA 0 2
#> 5 Shipped fast 🏁😀 1 1 2Translate emoji to and from text
emoji_to_text() replaces emoji with words (handy for accessibility and NLP preprocessing); text_to_emoji() is the inverse. as_emoji_name(), as_emoji_shortcode() and as_emoji() are the vector-level equivalents.
reviews %>% emoji_to_text(text, format = "name")
#> # A tibble: 5 × 1
#> text
#> <chr>
#> 1 Best purchase ever grinning facesmiling face with heart-eyes
#> 2 It broke after a day enraged face
#> 3 Does the job.
#> 4 Wearing my mask face with medical maskface with medical mask
#> 5 Shipped fast chequered flaggrinning face
reviews %>% emoji_to_text(text, format = "shortcode")
#> # A tibble: 5 × 1
#> text
#> <chr>
#> 1 Best purchase ever :grinning::heart_eyes:
#> 2 It broke after a day :rage:
#> 3 Does the job.
#> 4 Wearing my mask :mask::mask:
#> 5 Shipped fast :checkered_flag::grinning:You can also search the emoji catalogue by keyword:
emoji_search("happy")
#> # A tibble: 27 × 5
#> emoji name shortcode group keyword
#> <chr> <chr> <chr> <chr> <chr>
#> 1 😀 grinning face grinning Smileys … happy
#> 2 😃 grinning face with big eyes smiley Smileys … happy
#> 3 😄 grinning face with smiling eyes smile Smileys … happy
#> 4 😁 beaming face with smiling eyes grin Smileys … happy
#> 5 😆 grinning squinting face laughing Smileys … happy
#> 6 🤣 rolling on the floor laughing rofl Smileys … happy
#> 7 😂 face with tears of joy joy Smileys … happy
#> 8 🙂 slightly smiling face slightly_smiling_face Smileys … happy
#> 9 😇 smiling face with halo innocent Smileys … happy
#> 10 ☺ smiling face smiling_face Smileys … happy
#> # ℹ 17 more rowsLearn more
The introductory vignette (vignette("introduction", package = "tidyEmoji")) walks through a full analysis of a real corpus — counting, categorising, sentiment- and emotion-scoring emoji, and plotting the results.