Delete preprocessing_functionality.py

project_utilities/preprocessing_functionality.py

-import nltk
-import pandas
-from nltk.stem import WordNetLemmatizer, PorterStemmer
-from bs4 import BeautifulSoup
-import re
-
-lemmatizer = nltk.stem.WordNetLemmatizer()
-stemmer = nltk.PorterStemmer()
-
-
-def clean_text(text: str) -> str:
-    # Strip HTML & XML
-    text = BeautifulSoup(text, "lxml").text
-    text = re.sub(r'\|\|\|', r' ', text)
-    # Strip Hyperlinks & URLs
-    text = re.sub(r'http\S+', r'<URL>', text)
-    # Strip Non-word characters
-    text = re.sub(r'[^\w\s]', '', text)
-    # Convert string to lowercase
-    text = text.replace('x', '')
-    text = text.lower()
-    return text
-
-def tokenize_text(text):
-    untokenized_sentences = nltk.sent_tokenize(text)
-    tokenized_sentences = [tokenize_sentence(sentence) for sentence in untokenized_sentences]
-    tokens = [token for tokenized_sentence in tokenized_sentences for token in tokenized_sentence]
-    return tokens
-
-
-def tokenize_sentence(sentence):
-    tokenized_sentence = nltk.word_tokenize(sentence)
-    tokenized_sentence = [word for word in tokenized_sentence if len(word) > 2]
-    return tokenized_sentence
-
-
-def lemmatize_text(text: list):
-    lemmatized = [lemmatizer.lemmatize(word) for word in text]
-    return ' '.join(lemmatized)
-
-def stem_text(text: list):
-    stemmed = [stemmer.stem(word) for word in text]
-    return ' '.join(stemmed)
-
-
-
-
-if __name__ == '__main__':
-    print(stemmer.stem('connecting'))
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