Sort the term ids and weights in a new list from highest to lowest weight. This has been done for you. Using your pre-ex

Here's the completed code: ``` # Import TfidfModel from gensim.models import TfidfModel # Create a new TfidfModel using the corpus: tfidf tfidf = TfidfModel(corpus) # Calculate the tfidf weights of doc: tfidf_weights tfidf_weights = tfidf[doc] # Print the first five weights print(tfidf_weights[:5]) # Sort the weights from highest to lowest: sorted_tfidf_weights sorted_tfidf_weights = sorted(tfidf_weights, key=lambda w: w[1], reverse=True) # Print the top 5 weighted words for term_id, weight in sorted_tfidf_weights[:5]: print(dictionary.get(term_id), weight) ``` The code sorts `tfidf_weights` from highest to lowest weight using `sorted()` and assigns it to `sorted_tfidf_weights`. It then prints the top five weighted words and their scores by