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import re
import pandas as pd
import re
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
stop_words = set(stopwords.words('english'))
class preprocessing_pipeline:
def __init__(self,text):
self.text = text
def preprocess(self):
self.text = self.clean_text(self.text)
self.text = self.lowercase(self.text)
self.text = self.remove_punctuation(self.text)
self.text = self.remove_stopwords(self.text)
self.text = self.lemmatize_tokens(self.text)
return self.text
def clean_text(self , text: str) -> str:
text = text.strip()
text = text.replace("\n", " ").replace("\xa0", " ")
text = text.replace("“", "\"").replace("”", "\"").replace("–", "-")
return text
def lowercase(self, text: str) -> str:
return text.lower()
def remove_punctuation(self, text: str) -> str:
return re.sub(r"[^\w\s]", "", text)
def remove_stopwords(self, text: str) -> str:
tokens = word_tokenize(text)
return ' '.join([word for word in tokens if word not in stop_words])
def lemmatize_tokens(self, text: str) -> str:
tokens = word_tokenize(text)
lemmatizer = WordNetLemmatizer()
return ' '.join([lemmatizer.lemmatize(token) for token in tokens])
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