Cleaning the text and make it word cloud ready

clean_text.py

import re
import unicodedata
from nltk.corpus import stopwords
import nltk
def removetitle(text):
    return re.sub(r'.*:', '', text)

def removebrackets(text):
    return re.sub('[\(\[].*?[\)\]]', ' ', text)

def remove_accented_chars(text):
    return unicodedata.normalize('NFKD', text).encode('ascii', 'ignore').decode('utf-8', 'ignore')

def remove_special_chars(text, remove_digits=False):
    pattern = r'[^a-zA-Z0-9\s]' if not remove_digits else r'[^a-zA-Z\s]'
    return re.sub(pattern, '', text)

def remove_stopwords(text):
    stopword_list = stopwords.words('english')
    tokens = nltk.word_tokenize(text)
    tokens = [token.strip() for token in tokens]
    return ' '.join([token for token in tokens if token not in stopword_list])

def lemmatize(text):
    text = nlp(text)
    return ' '.join([word.lemma_ if word.lemma_ != '-PRON-' else word.text for word in text])

text_title=removetitle(text)
text_brackets=removebrackets(text_title)
text_clean=remove_accented_chars(text_brackets)
text_clean=text_clean.lower()
text_clean=remove_special_chars(text_clean)
text_clean=remove_stopwords(text_clean)