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

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. 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 # TF-IDF vectorizer = TfidfVectorizer() tfidf = vectorizer

# Features features = tfidf.toarray()[0] For text analysis tasks, traditional NLP techniques like