class_docstr: Class for detecting language of text.
class_docstr_tok: ["Class","for","detecting","language","of","text","."]
docstr: Process text to improve language id performance. Main emphasis is on tweets, this method removes shortened
  urls, hashtags, handles, and punctuation and emoji.
docstr_tok: ["Process","text","to","improve","language","id","performance",".","Main","emphasis","is","on","tweets",",","this","method","removes","shortened","urls",",","hashtags",",","handles",",","and","punctuation","and","emoji","."]
code: def clean_text(text):
      """
      Process text to improve language id performance. Main emphasis is on tweets, this method removes shortened
      urls, hashtags, handles, and punctuation and emoji.
      """
  
      for regex in LangIDProcessor.all_regexes:
          text = regex.sub(" ", text)
  
      text = emoji.emojize(text)
      text = emoji.replace_emoji(text, replace=" ")
  
      if text.strip():
          text = text.strip()
  
      return text
  
file_path: stanza/stanza/pipeline/langid_processor.py
imports: import emoji
  import re
  import stanza
  import torch
  from stanza.models.common.doc import Document
  from stanza.models.langid.model import LangIDBiLSTM
  from stanza.pipeline._constants import *
  from stanza.pipeline.processor import UDProcessor, register_processor
label_desc: Use image node_id 4 for calling the LangIDProcessor obj's underlying member method code with example usage: obj.clean_text(text) and returns: text