Subtitle Evil Dead 2013 Blu Ray | 1080p Dual Audi...

# TF-IDF vectorizer = TfidfVectorizer() tfidf = vectorizer.fit_transform([preprocessed_text])

# Preprocessing lemmatizer = WordNetLemmatizer() stop_words = set(stopwords.words('english'))

# Features features = tfidf.toarray()[0] subtitle Evil Dead 2013 Blu ray 1080p Dual Audi...

import re from collections import Counter from nltk.corpus import stopwords from nltk.tokenize import word_tokenize from nltk.stem import WordNetLemmatizer from sklearn.feature_extraction.text import TfidfVectorizer

preprocessed_text = preprocess_text(subtitle_text) # TF-IDF vectorizer = TfidfVectorizer() tfidf = vectorizer

# Sample subtitle text subtitle_text = """ ASH: Hi, Cheryl. What are you doing out here all alone? CHERYL: Ash, I was just looking for you. I couldn't sleep. ASH: (nervously) Oh, yeah. I was just, uh, getting some...fresh air. """

print("TF-IDF Features:", features) print("Feature Names:", vectorizer.get_feature_names_out()) For more complex tasks like classification, you could use the preprocessed text as input to a machine learning model. The features would then depend on the model's requirements (e.g., word embeddings for neural networks). Conclusion The approach to producing "deep features" for a subtitle file like that of "Evil Dead 2013 Blu ray 1080p Dual Audio" depends on the specific task you're interested in. For text analysis tasks, traditional NLP techniques like TF-IDF or more advanced methods involving deep learning can be applied. I couldn't sleep

def preprocess_text(text): tokens = word_tokenize(text.lower()) tokens = [re.sub(r'[^a-zA-Z]', '', token) for token in tokens] tokens = [token for token in tokens if token] tokens = [lemmatizer.lemmatize(token) for token in tokens if token not in stop_words] return " ".join(tokens)