preprocessing.py

import hazm
from cleantext import clean
import regex as re

def cleanhtml(raw_html):
    cleanr = re.compile('<.*?>')
    cleantext = re.sub(cleanr, '', raw_html)
    return cleantext

def cleaning(text):
    text = text.strip()
    
    # regular cleaning
    # https://pypi.org/project/clean-text/ >> works well for eng and de languages
    text = clean(text,
        fix_unicode=True,
        to_ascii=False,
        lower=True,
        no_line_breaks=True,
        no_urls=True,
        no_emails=True,
        no_phone_numbers=True,
        no_numbers=False,
        no_digits=False,
        no_currency_symbols=True,
        no_punct=False, #Keep the punc
        replace_with_url="",
        replace_with_email="",
        replace_with_phone_number="",
        replace_with_number="",
        replace_with_digit="0",
        replace_with_currency_symbol="",
    )

    # cleaning htmls
    text = cleanhtml(text)
    
    # normalizing > https://github.com/sobhe/hazm
    normalizer = hazm.Normalizer()
    text = normalizer.normalize(text)
    
    # removing wierd patterns
    wierd_pattern = re.compile("["
        u"\U0001F600-\U0001F64F"  # emoticons
        u"\U0001F300-\U0001F5FF"  # symbols & pictographs
        u"\U0001F680-\U0001F6FF"  # transport & map symbols
        u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
        u"\U00002702-\U000027B0"
        u"\U000024C2-\U0001F251"
        u"\U0001f926-\U0001f937"
        u'\U00010000-\U0010ffff'
        u"\u200d"
        u"\u2640-\u2642"
        u"\u2600-\u2B55"
        u"\u23cf"
        u"\u23e9"
        u"\u231a"
        u"\u3030"
        u"\ufe0f"
        u"\u2069"
        u"\u2066"
        # u"\u200c"
        u"\u2068"
        u"\u2067"
        "]+", flags=re.UNICODE)
    
    text = wierd_pattern.sub(r'', text)
    
    # removing extra spaces, hashtags
    text = re.sub("#", "", text)
    text = re.sub("\s+", " ", text)
    
    return text