import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer

sentences = final_text

# Initialize stopwords and stemmer
stop_words = set(stopwords.words('english'))
stemmer = PorterStemmer()

# Preprocessing for each sentence in the list
preprocessed_sentences = []

for sentence in sentences:
    # Tokenization
    tokens = word_tokenize(sentence)

    # Remove stopwords and apply stemming
    filtered_tokens = [stemmer.stem(word.lower()) for word in tokens if word.lower() not in stop_words]

    # Normalization (remove punctuation)
    normalized_tokens = [word for word in filtered_tokens if word not in string.punctuation]

    # Combine the normalized tokens back into a sentence
    preprocessed_sentence = " ".join(normalized_tokens)

    preprocessed_sentences.append(preprocessed_sentence)

# print(preprocessed_sentences)
for i in preprocessed_sentences:
    print(i)