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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.
install.packages("tidyEmoji")
# development version
# install.packages("devtools")
devtools::install_github("PursuitOfDataScience/tidyEmoji")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"))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 🏁😀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 1emoji_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… 0.572reviews %>% 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.
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 joyemoji_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.
reviews %>% emoji_dfm(text) %>% select(1:4)
#> # A tibble: 5 × 4
#> .row_number `😀` `😷` `🏁`
#> <int> <int> <int> <int>
#> 1 1 1 0 0
#> 2 2 0 0 0
#> 3 3 0 0 0
#> 4 4 0 2 0
#> 5 5 1 0 1The 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 2emoji_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 rowsThe 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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.