# Textual Features def get_textual_features(topic): # Initialize a simple Word2Vec model with a dummy document sentences = [topic.split()] model = Word2Vec(sentences, vector_size=100, min_count=1) vectors = [] for word in topic.split(): try: vectors.append(model.wv[word]) except KeyError: # Handle out-of-vocabulary words vectors.append(np.zeros(100)) textual_feature = np.mean(vectors, axis=0) # Average vector representation # TF-IDF tfidf = TfidfVectorizer().fit([topic]) tfidf_feature = tfidf.transform([topic]).toarray()[0] return np.concatenate([textual_feature, tfidf_feature])
import numpy as np from gensim.models import Word2Vec from sklearn.feature_extraction.text import TfidfVectorizer -AnimeRG- Naruto -2002- Complete Series Movie...
# Combine Features textual_feature = get_textual_features(topic) metadata_feature = get_metadata_features() like utilizing pre-trained language models (e.g.
# Metadata Features def get_metadata_features(): genres = ["Action", "Adventure", "Fantasy"] # Example genres genre_vector = [1 if g in genres else 0 for g in ["Action", "Adventure", "Fantasy", "Comedy"]] # Assuming a fixed set of genres release_year = 2002 complete_series = 1 # Binary feature return np.array([release_year, complete_series] + genre_vector) BERT) for textual features
# Sample data topic = "-AnimeRG- Naruto -2002- Complete Series Movie..."
deep_feature = np.concatenate([textual_feature, metadata_feature]) This example provides a basic outline. Real-world applications might involve more complex processing, like utilizing pre-trained language models (e.g., BERT) for textual features, integrating visual features from images or videos, and leveraging extensive metadata.