# Print the prepared feature print(data['dicited']) : This is a basic example, and you may want to fine-tune the preprocessing and entity recognition steps based on your specific use case. Additionally, you will need to download the required NLTK data using nltk.download('punkt') and nltk.download('stopwords') .
# Create a new feature 'dicited' that combines preprocessed text and entities data['dicited'] = data.apply(lambda row: (row['preprocessed_text'], row['entities']), axis=1) return data # Load data data = load_data('text_data.csv') dicited
# Lemmatize tokens lemmatizer = WordNetLemmatizer() lemmatized_tokens = [lemmatizer.lemmatize(t) for t in filtered_tokens] # Print the prepared feature print(data['dicited']) : This