Package {tidyEmoji}

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

Description

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. The bundled emoji sentiment lexicon is from the Emoji Sentiment Ranking of Kralj Novak et al. (2015) doi:10.1371/journal.pone.0144296, 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.

Output and naming contract

Every verb follows verb(data, text, ...), takes the text column unquoted, and returns a tibble. Columns added to your data carry a dotted ⁠.emoji_*⁠ prefix (.emoji, .emoji_name, .emoji_category, .emoji_sentiment, .emoji_n, ...) so they cannot collide with your own columns; new summary tibbles (e.g. emoji_frequency()) use bare names. group always refers to the Unicode top-level category (the term used by the underlying emoji::emojis table). Every glyph-to-metadata join is normalised through a codepoint key that strips the U+FE0F variation selector, so qualified and unqualified emoji forms resolve identically in every verb.

Author(s)

Maintainer: Youzhi Yu yuyouzhi666@icloud.com

See Also

Useful links:

• https://pursuitofdatascience.github.io/tidyEmoji/

• Report bugs at https://github.com/PursuitOfDataScience/tidyEmoji/issues

Vector helpers: convert emoji to/from names and shortcodes

Description

Small vector-level helpers for ad-hoc use. They do not take a data frame.

Usage

as_emoji_name(x)

as_emoji_shortcode(x)

as_emoji(x)

Arguments

Details

• as_emoji_name(x) maps emoji glyphs to their Unicode names.

• as_emoji_shortcode(x) maps emoji glyphs to their first shortcode.

• as_emoji(x) maps shortcodes/names to the emoji glyph (emojize).

All three resolve through emoji_key(), so qualified emoji (carrying U+FE0F) and unqualified forms resolve identically. Unmatched inputs return NA.

Value

A character vector the same length as x.

See Also

emoji_to_text(), text_to_emoji() for the data-frame verbs.

Examples

as_emoji_name(c("\U0001f600", "\u2764\ufe0f"))
as_emoji_shortcode(c("\U0001f600", "\u2764\ufe0f"))
as_emoji(c("grinning", "heart"))

Emoji category to unicode crosswalk

Description

A table with one row per Unicode category, listing every emoji glyph in that category as a single |-separated string.

Usage

category_unicode_crosswalk

Format

A data frame with two columns:

category

The Unicode category (10 categories).

unicodes

The emoji glyphs in the category, separated by |.

Source

Derived from the emojis table of the emoji package; rebuilt by data-raw/crosswalks.R.

Categorise each row by the emoji categories it contains

Description

emoji_categorize() keeps the rows of data that contain emoji and adds a .emoji_category column listing the distinct Unicode categories present in that row (for example "Smileys & Emotion"), separated by | when a row spans more than one category.

Usage

emoji_categorize(data, text)

Arguments

Value

data, as a tibble, filtered to the rows containing emoji and with an added .emoji_category column.

Examples

df <- data.frame(text = c("smile \U0001f600",
                          "flag \U0001f3c1\U0001f600",
                          "nothing"))
emoji_categorize(df, text)

Emoji co-occurrence counts, with an optional diagonal

Description

emoji_cooccurrence() is emoji_pairs() under another name, with one addition: diagonal = TRUE also returns the item1 == item2 rows, whose n is the number of documents containing that emoji (the diagonal of the co-occurrence matrix, i.e. its document frequency).

Usage

emoji_cooccurrence(data, text, doc_id = NULL, diagonal = FALSE, sort = TRUE)

Arguments

Value

A tibble with columns item1, item2 and n.

See Also

emoji_pairs(), emoji_ngrams().

Examples

df <- data.frame(text = c("\U0001f602\U0001f60d", "\U0001f602"))
emoji_cooccurrence(df, text, diagonal = TRUE)

Emoji density per character and per token

Description

emoji_density() measures how emoji-heavy each text is: the number of emoji per character and per whitespace-delimited token. Rows with no emoji get densities of 0; rows whose text is NA or empty get NA.

Usage

emoji_density(data, text)

Arguments

Value

data, as a tibble, with added columns .emoji_n, .emoji_per_char (emoji per character of text) and .emoji_per_token (emoji per whitespace-delimited token).

See Also

emoji_position(), emoji_ratio().

Examples

df <- data.frame(text = c("hi \U0001f600", "\U0001f600\U0001f600", "plain"))
emoji_density(df, text)

Document-by-emoji feature matrix

Description

emoji_dfm() turns a text column into a wide, model-ready table with one row per document and one column per emoji, weighted by raw counts, binary presence or tf-idf. All documents are kept, including those with no emoji (all-zero rows), so the result aligns row-for-row with the corpus and can be bound to outcome columns for tidymodels-style workflows.

Usage

emoji_dfm(data, text, doc_id = NULL, weighting = c("count", "binary", "tfidf"))

Arguments

Details

By default every row of data is a document and the first output column, .row_number, is its position in data (matching emoji_extract_unnest()). Give doc_id to aggregate rows sharing an id into one document; the id column keeps its name. Emoji columns are named by the glyph itself, canonicalised through the package's codepoint key (so qualified and unqualified forms count as one feature), and ordered by descending total count (ties broken by glyph).

For weighting = "tfidf", the cell for emoji e in document d is count(d, e) * log(N / df(e)), where N is the number of documents and df(e) the number of documents containing e. An emoji that appears in every document therefore scores 0.

Value

A tibble with one row per document: .row_number (or the doc_id column) followed by one numeric column per emoji. Zero emoji in the corpus yields just the document column.

See Also

emoji_frequency() for corpus totals; emoji_tokens() for the long form this widens.

Examples

df <- data.frame(text = c("\U0001f600\U0001f600 fun", "\U0001f621",
                          "no emoji"))
emoji_dfm(df, text)
emoji_dfm(df, text, weighting = "binary")
emoji_dfm(df, text, weighting = "tfidf")

Emoji emotion profiles (the 8 Plutchik emotions)

Description

emoji_emotion() scores each row's emoji across the eight Plutchik emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, trust) using the bundled EmoTag1200 lexicon (Shoeb & de Melo, 2020). Scores each range from 0 to 1 and are averaged over the emoji in the row that appear in the lexicon.

Usage

emoji_emotion(data, text, lexicon = "emotag1200", long = FALSE)

Arguments

Value

data, as a tibble, with emotion columns added. Rows without emoji, or whose emoji are absent from the lexicon, receive NA scores.

References

Shoeb AAM, de Melo G (2020). EmoTag1200: Understanding the Association between Emojis and Emotions. EMNLP 2020. https://aclanthology.org/2020.emnlp-main.720/. Data released under the MIT licence.

See Also

emoji_emotion_lexicon for the underlying scores; emoji_emotion_label() for the dominant emotion per row; emoji_sentiment() for valence.

Examples

df <- data.frame(text = c("love it \U0001f60d", "scary \U0001f628", "meh"))
emoji_emotion(df, text)
emoji_emotion(df, text, long = TRUE)

The dominant emoji emotion per row

Description

emoji_emotion_label() adds .emoji_emotion, the emotion with the highest mean score among the row's emoji (using emoji_emotion()). Ties are broken in Plutchik order; rows with no scored emoji receive NA.

Usage

emoji_emotion_label(data, text, lexicon = "emotag1200")

Arguments

Value

data, as a tibble, with a .emoji_emotion column added.

Examples

df <- data.frame(text = c("love it \U0001f60d", "scary \U0001f628", "meh"))
emoji_emotion_label(df, text)

Emoji emotion lexicon (EmoTag1200)

Description

Human-annotated emotion-association scores (each from 0 to 1) for the eight Plutchik emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, trust), for the 150 most popular Twitter emoji, from EmoTag1200.

Usage

emoji_emotion_lexicon

Format

A data frame with one row per emoji and the columns:

key

Codepoint-normalised key (U+FE0F stripped) for robust joining.

emoji

The emoji glyph (unqualified form, as stored by the source).

name

The emoji's Unicode name.

anger, anticipation, disgust, fear, joy, sadness, surprise, trust

Emotion-association scores, each from 0 to 1.

Source

Shoeb AAM, de Melo G (2020). EmoTag1200: Understanding the Association between Emojis and Emotions. EMNLP 2020. https://aclanthology.org/2020.emnlp-main.720/. Data from https://github.com/abushoeb/EmoTag, released under the MIT licence. Processed by data-raw/emoji_emotion_lexicon.R.

Add a list-column of the emoji found in each row

Description

emoji_extract_nest() returns data unchanged except for an added list-column, .emoji_unicode, holding the emoji found in each row. Detection is grapheme-aware, so skin-tone modifiers and ZWJ sequences (for example family emoji) are kept intact as a single emoji.

Usage

emoji_extract_nest(data, text)

Arguments

Value

data with an added list-column .emoji_unicode.

See Also

emoji_extract_unnest() for a long, counted form and emoji_tokens() for one row per emoji with metadata.

Examples

df <- data.frame(text = c("hi \U0001f600\U0001f603", "none"))
emoji_extract_nest(df, text)

Emoji counts per row, in long (tidy) form

Description

emoji_extract_unnest() returns one row per (row, emoji) pair with a count, dropping rows that contain no emoji. .row_number refers to the position of the entry in data.

Usage

emoji_extract_unnest(data, text)

Arguments

Value

A tibble with columns .row_number, .emoji_unicode and .emoji_count.

Examples

df <- data.frame(text = c("hi \U0001f600\U0001f600", "none", "\U0001f44b"))
emoji_extract_unnest(df, text)

Keep only the rows whose text contains emoji

Description

emoji_filter() returns the rows of data whose text column contains at least one emoji, preserving every original column. emoji_tweets() is a synonym retained for backward compatibility.

Usage

emoji_filter(data, text)

emoji_tweets(data, text)

Arguments

Value

A tibble containing only the rows with at least one emoji. The result is always a plain (ungrouped) tibble, whatever the class or grouping of the input.

Examples

df <- data.frame(text = c("hi \U0001f600", "no emoji", "bye \U0001f44b"))
emoji_filter(df, text)

Frequency of every emoji in a text column

Description

emoji_frequency() counts how often each emoji appears across the whole text column (an entry containing the same emoji twice contributes 2) and returns a tibble sorted by descending count, with each emoji's name, shortcode and category.

Usage

emoji_frequency(data, text)

Arguments

Value

A tibble with columns emoji, name, shortcode, group and n, sorted by descending n with ties broken by the glyph so the order is deterministic.

See Also

top_n_emojis() for just the most frequent emoji.

Examples

df <- data.frame(text = c("\U0001f600\U0001f600", "\U0001f621"))
emoji_frequency(df, text)

List bundled emoji lexicons

Description

emoji_lexicons() returns a tibble describing the lexicons bundled with tidyEmoji and any user-registered ones: their name, type (sentiment or emotion), dimensions, number of emoji, source and licence.

Usage

emoji_lexicons()

Value

A tibble with columns name, type, dimensions, n, source, licence.

See Also

register_emoji_lexicon() to add your own; emoji_score() to score text against any lexicon.

Examples

emoji_lexicons()

Consecutive emoji sequences (n-grams)

Description

emoji_ngrams() slides a window of n over each row's emoji, in reading order (any text between the emoji is ignored), and returns one row per n-gram occurrence. Repeated emoji are kept: a row containing the same emoji twice in a row yields a bigram of that emoji with itself. This is the emoji analogue of tidytext::unnest_tokens(..., token = "ngrams") and feeds sequence / Markov-style analyses of how emoji chain together.

Usage

emoji_ngrams(data, text, n = 2, sep = " ")

Arguments

Value

A tibble with columns .row_number (position of the entry in data), .position (where the n-gram starts within the row's emoji sequence) and .emoji_ngram. Rows with fewer than n emoji contribute nothing.

See Also

emoji_pairs() for order-free co-occurrence; emoji_extract_unnest() for the underlying one-emoji-per-row form.

Examples

df <- data.frame(text = c("\U0001f602\U0001f60d\U0001f389", "\U0001f602"))
emoji_ngrams(df, text)
emoji_ngrams(df, text, n = 3)

Co-occurring emoji pairs

Description

emoji_pairs() returns a tidy edge list of the emoji that appear together in the same document: one row per pair with the number of documents in which the pair co-occurs. By default every row of data is a document; give doc_id to treat all rows sharing an id (a conversation, a user, a day) as one document. The output mirrors widyr::pairwise_count() (item1, item2, n) and pipes straight into igraph::graph_from_data_frame(), tidygraph or ggraph.

Usage

emoji_pairs(data, text, doc_id = NULL, directed = FALSE, sort = TRUE)

Arguments

Details

Glyphs are canonicalised through the package's codepoint key, so qualified and unqualified forms of the same emoji (with/without U+FE0F) count as one node. Pairs are between distinct emoji: repeats of the same emoji in a document do not pair with themselves (see emoji_cooccurrence() for the diagonal).

Value

A tibble with columns item1, item2 and n. Empty (but typed) when no document contains two distinct emoji.

See Also

emoji_cooccurrence() for the same counts with an optional diagonal; emoji_ngrams() for consecutive sequences.

Examples

df <- data.frame(text = c("fun \U0001f602\U0001f60d",
                          "\U0001f602\U0001f60d\U0001f389",
                          "just \U0001f602"))
emoji_pairs(df, text)
emoji_pairs(df, text, directed = TRUE)

Where do emoji sit within each text?

Description

emoji_position() reports, for each row, the character position of the first and last emoji and the mean relative position of all emoji occurrences, from 0 (the very start of the text) to 1 (the very end). The Emoji Sentiment Ranking (Kralj Novak et al., 2015) tracks the same relative position, and it is a studied signal: emoji cluster near the end of messages.

Usage

emoji_position(data, text)

Arguments

Details

The relative position of an occurrence starting at character s in a text of L characters is (s - 1) / (L - 1) (taken as 0 when L <= 1). Positions are counted in characters (code points), the same unit as substr().

Value

data, as a tibble, with added columns .emoji_n, .emoji_first and .emoji_last (character positions where the first/last emoji start) and .emoji_rel_position (mean relative position in ⁠[0, 1]⁠). Rows without emoji get NA positions.

See Also

emoji_density() and emoji_ratio() for intensity metrics.

Examples

df <- data.frame(text = c("\U0001f600 leading", "trailing \U0001f600",
                          "none"))
emoji_position(df, text)

What share of the text is emoji — and is it emoji-only?

Description

emoji_ratio() reports, per row, the share of the text's characters that belong to emoji, and whether the text is emoji-only (nothing left after removing emoji and whitespace). "Emoji-only" messages are a studied signal in social-media research and a useful filter in practice.

Usage

emoji_ratio(data, text)

Arguments

Details

The ratio is computed over characters (code points), so a multi-code-point emoji (a ZWJ family, a skin-tone sequence) contributes all of its characters.

Value

data, as a tibble, with added columns .emoji_ratio (emoji characters / all characters, 0 when there are no emoji) and .emoji_only (TRUE when the text contains emoji and nothing else but whitespace). NA text gets NA in both.

See Also

emoji_position(), emoji_density(); emoji_filter() to keep emoji-bearing rows.

Examples

df <- data.frame(text = c("\U0001f600\U0001f389", "half \U0001f600", "no"))
emoji_ratio(df, text)

Score emoji in a text column against any lexicon

Description

emoji_score() is the generic scorer that the friendly verbs (emoji_sentiment(), emoji_emotion()) sit on top of. It joins each row's emoji to lexicon through emoji_key() and returns the per-row mean of the score column, plus the number of emoji scored. Bring your own lexicon, or name a bundled / registered one.

Usage

emoji_score(data, text, lexicon, by = "emoji", score = NULL)

Arguments

Value

data, as a tibble, with .emoji_score (per-row mean), .emoji_n_scored (emoji found in the lexicon) and .emoji_n (total emoji) added. For the multi-dimensional "emotag1200" lexicon the score is the mean over its eight emotion dimensions; use emoji_emotion() for the per-emotion profile.

See Also

emoji_lexicons(), register_emoji_lexicon().

Examples

df <- data.frame(text = c("love \U0001f60d", "angry \U0001f621", "meh"))
emoji_score(df, text, lexicon = "novak2015")

# a bring-your-own lexicon
own <- data.frame(emoji = c("\U0001f600", "\U0001f621"),
                  score = c(0.9, -0.8))
emoji_score(df, text, lexicon = own)

Search emoji by keyword, name or shortcode

Description

emoji_search() finds emoji whose Unicode keywords, name or shortcodes match a query (case-insensitive, substring match). It returns a tidy tibble of matches with the glyph, name, shortcode, category and the matching keywords, ready for further inspection or piping into other verbs.

Usage

emoji_search(query)

Arguments

Value

A tibble with columns emoji, name, shortcode, group and keyword (the keywords of the emoji that contained the match, collapsed with ⁠, ⁠).

Examples

emoji_search("happy")
emoji_search("heart")

Score the sentiment of the emoji in each row

Description

emoji_sentiment() adds the mean emoji sentiment of each row, based on the Emoji Sentiment Ranking lexicon (see emoji_sentiment_lexicon). Scores range from -1 (negative) through 0 (neutral) to +1 (positive). Rows that contain no emoji, or whose emoji are absent from the lexicon, receive NA.

Usage

emoji_sentiment(data, text, lexicon = "novak2015")

Arguments

Details

Detection is grapheme-aware. Some lexicon entries are stored as unqualified, text-presentation code points (notably the bare heart, U+2764, without the U+FE0F variation selector); those are not treated as emoji in your text, so they are neither counted nor scored. Supply the emoji-presentation (qualified) form and it resolves normally. See emoji_sentiment_lexicon for the full picture.

Value

data, as a tibble, with added columns .emoji_n (the number of emoji in the row), .emoji_n_scored (the number of emoji that actually appear in the lexicon), and .emoji_sentiment (the mean sentiment of the scored emoji).

References

Kralj Novak P, Smailovic J, Sluban B, Mozetic I (2015) Sentiment of Emojis. PLoS ONE 10(12): e0144296. doi:10.1371/journal.pone.0144296

See Also

emoji_sentiment_lexicon for the underlying scores; emoji_score() for scoring against any lexicon; emoji_emotion() for discrete emotions.

Examples

df <- data.frame(text = c("love it \U0001f60d", "awful \U0001f621", "meh"))
emoji_sentiment(df, text)

Emoji Sentiment Ranking lexicon

Description

Sentiment scores for emoji, from the Emoji Sentiment Ranking 1.0, computed from ~70,000 tweets in 13 European languages annotated for sentiment. The sentiment_score is (positive - negative) / occurrences, ranging from -1 (negative) to +1 (positive); sentiment_label is derived from its sign.

Usage

emoji_sentiment_lexicon

Format

A data frame with one row per emoji and the columns:

emoji

The emoji glyph.

occurrences

Number of times the emoji was observed.

position

Mean position of the emoji within its text (0-1).

negative, neutral, positive

Annotation counts for each class.

sentiment_score

Sentiment score from -1 to 1.

sentiment_label

"negative", "neutral" or "positive".

unicode_name

The official Unicode character name.

unicode_block

The Unicode block.

Detection limitations

Many of the glyphs in this lexicon are stored in their unqualified, text-presentation form: a single code point with no U+FE0F emoji-presentation variation selector. The best-known is the bare heart, U+2764; others include the white smiling face (U+263A), the heavy check mark (U+2714) and the black rightwards arrow (U+27A1). The lexicon also contains characters that are not emoji at all (box-drawing characters, the copyright and registered signs, the replacement character), inherited from the tweets it was built from.

The grapheme-aware detection used throughout the package does not treat these text-presentation code points as emoji, so a row whose only "emoji" is one of them is not counted or scored – it behaves as if it contained no emoji. This affects detection only, never the join: supply the qualified form (the red heart U+2764 U+FE0F, say) and it resolves to the same lexicon entry, because every lookup goes through a codepoint key that ignores U+FE0F.

Source

Kralj Novak P, Smailovic J, Sluban B, Mozetic I (2015) Sentiment of Emojis. PLoS ONE 10(12): e0144296. doi:10.1371/journal.pone.0144296. Data from https://hdl.handle.net/11356/1048, released under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) licence. Processed by data-raw/emoji_sentiment_lexicon.R.

Summarise emoji presence in a text column

Description

emoji_summary() reports how many entries in a text column contain at least one emoji, alongside the total number of entries. An entry is counted once regardless of how many emoji it holds.

Usage

emoji_summary(data, text)

Arguments

Value

A one-row tibble with columns n_with_emoji (entries containing at least one emoji) and n_total (all entries).

See Also

emoji_filter() to keep the emoji-bearing rows themselves.

Examples

df <- data.frame(text = c("I love R \U0001f600",
                          "no emoji here",
                          "flags \U0001f3c1\U0001f600"))
emoji_summary(df, text)

Replace emoji in a text column with words (demojize)

Description

emoji_to_text() returns a copy of data with its text column rewritten so that every emoji is replaced by its name or shortcode. This is useful for accessibility (screen readers) and as an NLP normalisation step before tokenising. Detection is grapheme-aware and joins go through emoji_key(), so emoji carrying the U+FE0F variation selector still resolve.

Usage

emoji_to_text(data, text, format = c("name", "shortcode"), wrap = ":{x}:")

Arguments

Value

data, as a tibble, with the text column rewritten in place (same column name). NA entries stay NA, and emoji with no known name are left in place unchanged.

See Also

text_to_emoji() for the inverse (emojize); as_emoji_name(), as_emoji_shortcode(), as_emoji() for vector helpers.

Examples

df <- data.frame(text = "great \U0001f600 love \u2764\ufe0f")
emoji_to_text(df, text, format = "name")
emoji_to_text(df, text, format = "shortcode")

Tidy emoji tokens, one row per occurrence with metadata

Description

emoji_tokens() expands data to one row per emoji occurrence (in reading order), keeping the original columns and adding the glyph together with its name, category and sentiment score. This mirrors the one-token-per-row shape familiar from tidy text mining and is convenient for counting, joining and plotting.

Usage

emoji_tokens(data, text)

Arguments

Value

A tibble with the original columns plus .emoji, .emoji_name, .emoji_category and .emoji_sentiment. Rows without emoji are dropped.

See Also

emoji_frequency() for corpus-level counts and emoji_sentiment() for per-row sentiment.

Examples

df <- data.frame(id = 1:2, text = c("great \U0001f600", "bad \U0001f621"))
emoji_tokens(df, text)

Emoji name, unicode and category crosswalk

Description

A table with one row per emoji name: each emoji glyph appears once for every GitHub-style name it is known by, so a single unicode can occur on several rows (for example the grinning face is both "grinning" and "grinning_face").

Usage

emoji_unicode_crosswalk

Format

A data frame with four columns:

emoji_name

The emoji name / shortcode (e.g. "grinning").

unicode

The emoji glyph.

emoji_category

The Unicode category the emoji belongs to.

key

Codepoint-normalised key (U+FE0F stripped) for robust joining.

Source

Derived from the emojis table of the emoji package; rebuilt by data-raw/crosswalks.R.

Register a custom emoji lexicon

Description

register_emoji_lexicon() adds a user-supplied lexicon to the in-session registry so it can be referenced by name in emoji_score(), emoji_sentiment() or emoji_emotion(). The lexicon is normalised through the package's codepoint key (U+FE0F stripped), so a lexicon keyed on unqualified glyphs still matches qualified text.

Usage

register_emoji_lexicon(name, tbl, by = "emoji")

Arguments

Details

Registration lasts for the session; it is not written to disk.

Value

Invisibly, the registered lexicon (with an added key column).

See Also

emoji_lexicons() to list lexicons; emoji_score() to use one.

Examples

my_lex <- data.frame(
  emoji = c("\U0001f600", "\U0001f621"),
  score = c(0.9, -0.8)
)
register_emoji_lexicon("mine", my_lex)
emoji_lexicons()
emoji_score(data.frame(text = "great \U0001f600"), text, lexicon = "mine")

Replace shortcodes with emoji (emojize)

Description

text_to_emoji() returns a copy of data with its text column rewritten so that every ⁠:shortcode:⁠ token is replaced by the corresponding emoji glyph (the inverse of emoji_to_text() with format = "shortcode"). Shortcodes that do not match a known emoji are left unchanged.

Usage

text_to_emoji(data, text)

Arguments

Details

A shortcode token is a colon, one or more of A-Z, a-z, 0-9, ⁠_⁠, + or -, and a closing colon. Restricting the token this way means colons used for other purposes – clock times, URLs, ratios, ordinary punctuation – cannot swallow a following shortcode: "meet at 10:30 :wave:" still emojizes the wave.

Value

data, as a tibble, with the text column rewritten in place. NA entries stay NA.

See Also

emoji_to_text(); as_emoji() for the vector helper.

Examples

df <- data.frame(text = "hi :grinning: bye :waving_hand:")
text_to_emoji(df, text)

# colons elsewhere in the text do not interfere
text_to_emoji(data.frame(text = "https://example.org at 10:30 :grinning:"),
              text)

The most frequent emoji in a text column

Description

top_n_emojis() returns the n most frequent emoji. By default each emoji (unicode) appears on a single row; set duplicated = TRUE to list every name an emoji is known by, so glyphs that share several names occupy several rows.

Usage

top_n_emojis(
  data,
  text,
  n = 20,
  duplicated = FALSE,
  duplicated_unicode = lifecycle::deprecated()
)

Arguments

Value

A tibble with columns emoji_name, unicode, emoji_category and n.

See Also

emoji_frequency() for the full distribution.

Examples

df <- data.frame(text = c("\U0001f600\U0001f600\U0001f3c1", "\U0001f621"))
top_n_emojis(df, text, n = 2)