class_docstr: Create a doc instance of text, obtain cleaned, readable text and
  metadata from this doc.
  
  Properties:
  raw: incoming, unedited text
  language: 2-letter code for the language of the text
  is_detected_language: is the language detected or specified beforehand
  is_reliable_language: is the language specified or was it reliably detected
  hint_language: language you expect your text to be
  _spacy_nlps: nested dictionary {lang: {model_id: model}} with loaded spacy language modules
docstr: Emojis detected using SpaCy matcher over the cleaned content, with unicode name and
  sentiment score.
  
  >>> from pprint import pprint
  >>> from textpipe.doc import Doc
  >>> pprint(Doc('Test with emoji 😀 😋 ').emojis)
  [('😀', 'GRINNING FACE', 0.571753986332574),
   ('😋', 'FACE SAVOURING DELICIOUS FOOD', 0.6335149863760218)]
docstr_tok: ["Emojis","detected","using","SpaCy","matcher","over","the","cleaned","content",",","with","unicode","name","and","sentiment","score",".",">",">",">","from","pprint","import","pprint",">",">",">","from","textpipe.doc","import","Doc",">",">",">","pprint","(","Doc","(","'Test","with","emoji","\ud83d\ude00","\ud83d\ude0b","'",")",".emojis",")","[","(","'\ud83d\ude00","'",",","'GRINNING","FACE","'",",","0.571753986332574",")",",","(","'\ud83d\ude0b","'",",","'FACE","SAVOURING","DELICIOUS","FOOD","'",",","0.6335149863760218",")","]"]
code: def emojis(self):
      """
      Emojis detected using SpaCy matcher over the cleaned content, with unicode name and
      sentiment score.
  
      >>> from pprint import pprint
      >>> from textpipe.doc import Doc
      >>> pprint(Doc('Test with emoji 😀 😋 ').emojis)
      [('😀', 'GRINNING FACE', 0.571753986332574),
       ('😋', 'FACE SAVOURING DELICIOUS FOOD', 0.6335149863760218)]
      """
      detected_emojis = []
      matcher = spacy.matcher.Matcher(self._spacy_doc.vocab)
      for emoji, unicode_name in EMOJI_TO_UNICODE_NAME.items():
          matcher.add(unicode_name, None, [{"ORTH": emoji}])
  
      for emoji, unicode_name in self.match(matcher):
          detected_emojis.append(
              (emoji, unicode_name, EMOJI_TO_SENTIMENT[emoji])
          )
  
      return detected_emojis
  
imports: import functools
  import re
  import unicodedata
  from collections import Counter
  from urllib.parse import urlparse
  import cld2
  import numpy
  import spacy
  import spacy.matcher
  import textacy
  import textacy.ke
  import textacy.text_utils
  from bs4 import BeautifulSoup
  from datasketch import MinHash
  from gensim.models.keyedvectors import KeyedVectors
  from gensim.summarization.summarizer import summarize
  from textpipe.data.emoji import EMOJI_TO_UNICODE_NAME, EMOJI_TO_SENTIMENT
  from textpipe.wrappers import RedisKeyedVectors
  from textpipe.util import getattr_
label_desc: Use image node_id 13 for calling the Doc obj's underlying member method code with example usage: obj.emojis() and returns: detected_emojis