# Combine Features textual_feature = get_textual_features(topic) metadata_feature = get_metadata_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.
# 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])
