Step 10: Text Preprocessing

Before analyzing the text, it’s often helpful to clean it up a bit. This can involve converting all text to lower case, removing punctuation, removing stop words (common words like “and”, “the”, “a”, which don’t add much meaning), and stemming or lemmatizing (reducing words to their root form).

from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
import re
# Initialize a PorterStemmer object to perform stemming
stemmer = PorterStemmer()
# Define a function to preprocess the text
def preprocess_text(text):
    # Convert to lower case
    text = text.lower()
    # Remove punctuation
    text = re.sub(r'[^\w\s]', '', text)
    # Remove stop words and stem the words
    text = ' '.join([stemmer.stem(word) for word in text.split() if word not in stopwords.words('english')])
    return text
# Apply the function to the review_body column
df[&apos...