spacymoji: emoji for spaCy
spaCy extension and pipeline component
for adding emoji meta data to Doc objects. Detects emoji consisting of one
or more unicode characters, and can optionally merge multi-char emoji (combined
pictures, emoji with skin tone modifiers) into one token. Human-readable emoji
descriptions are added as a custom attribute, and an optional lookup table can
be provided for your own descriptions. The extension sets the custom Doc,
Token and Span attributes ._.is_emoji, ._.emoji_desc, ._.has_emoji and ._.emoji. You can read more about custom pipeline components and extension attributes here.
Emoji are matched using spaCy's PhraseMatcher, and looked up in the data
table provided by the emoji package.
⏳ Installation
spacymoji requires spacy v3.0.0 or higher. For spaCy v2.x, instally spacymoji==2.0.0.
pip install spacymoji☝️ Usage
Import the component and add it anywhere in your pipeline using the string
name of the "emoji" component factory:
import spacy
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe("emoji", first=True)
doc = nlp("This is a test 😻 👍🏿")
assert doc._.has_emoji is True
assert doc[2:5]._.has_emoji is True
assert doc[0]._.is_emoji is False
assert doc[4]._.is_emoji is True
assert doc[5]._.emoji_desc == "thumbs up dark skin tone"
assert len(doc._.emoji) == 2
assert doc._.emoji[1] == ("👍🏿", 5, "thumbs up dark skin tone")spacymoji only cares about the token text, so you can use it on a blank
Language instance (it should work for all
available languages!), or in
a pipeline with a loaded pipeline. If your pipeline
includes a tagger, parser and entity recognizer, make sure to add the emoji
component as first=True, so the spans are merged right after tokenization,
and before the document is parsed. If your text contains a lot of emoji, this
might even give you a nice boost in parser accuracy.
Available attributes
The extension sets attributes on the Doc, Span and Token. You can
change the attribute names (and other parameters of the Emoji component) by passing
them via the config parameter in the nlp.add_pipe(...) method. For more details
on custom components and attributes, see the
processing pipelines documentation.
| Attribute | Type | Description |
|---|---|---|
Token._.is_emoji |
bool | Whether the token is an emoji. |
Token._.emoji_desc |
str | A human-readable description of the emoji. |
Doc._.has_emoji |
bool | Whether the document contains emoji. |
Doc._.emoji |
List[Tuple[str, int, str]] | (emoji, index, description) tuples of the document's emoji. |
Span._.has_emoji |
bool | Whether the span contains emoji. |
Span._.emoji |
List[Tuple[str, int, str]] | (emoji, index, description) tuples of the span's emoji. |
Settings
You can configure the emoji factory by setting any of the following parameters in
the config dictionary:
| Setting | Type | Description |
|---|---|---|
attrs |
Tuple[str, str, str, str] | Attributes to set on the ._ property. Defaults to ('has_emoji', 'is_emoji', 'emoji_desc', 'emoji'). |
pattern_id |
str | ID of match pattern, defaults to 'EMOJI'. Can be changed to avoid ID conflicts. |
merge_spans |
bool | Merge spans containing multi-character emoji, defaults to True. Will only merge combined emoji resulting in one icon, not sequences. |
lookup |
Dict[str, str] | Optional lookup table that maps emoji strings to custom descriptions, e.g. translations or other annotations. |
emoji_config = {"attrs": ("has_e", "is_e", "e_desc", "e"), lookup={"👨🎤": "David Bowie"})
nlp.add_pipe(emoji, first=True, config=emoji_config)
doc = nlp("We can be 👨🎤 heroes")
assert doc[3]._.is_e
assert doc[3]._.e_desc == "David Bowie"If you're training a pipeline, you can define the component config in your config.cfg:
[nlp]
pipeline = ["emoji", "ner"]
# ...
[components.emoji]
factory = "emoji"
merge_spans = false