Dicited Online

# Lemmatize tokens lemmatizer = WordNetLemmatizer() lemmatized_tokens = [lemmatizer.lemmatize(t) for t in filtered_tokens]

# Extract entities data['entities'] = data[text_column].apply(extract_entities) dicited

# Prepare feature data = prepare_dicited_feature(data, 'text_column') dicited

# Remove stopwords stop_words = set(stopwords.words('english')) filtered_tokens = [t for t in tokens if t.lower() not in stop_words] dicited

# 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')

# 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') .