Built this module in Python 3 to process and clean strings. It removes emojis, words that are smaller than a certain length, etc.

nlp_lib.py

import re
from string import digits, punctuation
from nltk.stem import SnowballStemmer
from nltk.tokenize import word_tokenize


class ProcessTextMethods:
    def __init__(self):
        self.stemmer = SnowballStemmer("english")
        self.emoji_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"
                                        "]+", flags=re.UNICODE)
        # exclude words that are smaller than:
        self.minimum_word_size_allowed = 3

    def nlp_clean_text(self, input_string):
        """
            Example: input_string = ' we Should hodl 3? an$D hodler 2 moar 34'
        """
        input_string_clean = input_string.lower()
        # clear weird accents
        input_string_clean = re.sub('’', '', input_string_clean)
        input_string_clean = re.sub('´', '', input_string_clean)
        # clean URLs
        input_string_clean = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', '', input_string_clean)
        input_string_clean = re.sub('www.(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', '', input_string_clean)
        # clean emojis
        input_string_clean = re.sub(self.emoji_pattern, '', input_string_clean)
        input_string_clean = input_string_clean.translate({ord(k): ' ' for k in digits})
        input_string_clean = input_string_clean.translate({ord(k): ' ' for k in punctuation})
        input_string_clean = ' '.join([word for word in input_string_clean.split() if len(word) >= self.minimum_word_size_allowed])
        return(input_string_clean)

    def word_ocurrence_counter(self, key, input_string_clean):
        """
            Example: key = 'hodl'
        """
        if (input_string_clean is not None) and (input_string_clean != ''):
            count = len([m.start() for m in re.finditer(key, input_string_clean)])
        else:
            count = 0
        return count

    def stem_input_string(self, input_string):
        # stem comment
        words = word_tokenize(input_string)
        stem_text = list()
        for word in words:
            stem_text.append(self.stemmer.stem(word))
        input_string_clean = ' '.join(stem_text)
        return input_string_clean