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tidyEmoji helps you discover, count, categorise and sentiment-score the emoji in any text column — social-media posts, product reviews, chat logs, survey responses, support tickets — and hands the result back 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
#> emoji_tweets total_tweets
#> <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 × 3
#> text .emoji_n .emoji_sentiment
#> <chr> <int> <dbl>
#> 1 Best purchase ever 😀😍 2 0.625
#> 2 It broke after a day 😡 1 -0.173
#> 3 Does the job. 0 NA
#> 4 Wearing my mask 😷😷 2 -0.171
#> 5 Shipped fast 🏁😀 2 0.572emoji_sentiment() uses the bundled Emoji
Sentiment Ranking lexicon (Kralj Novak et al., 2015). See the
package vignette for a fuller tour.
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.
Health stats visible at Monitor.