LDA model

Now it's time to build the LDA model. Using the dictionary and corpus, you are ready to discover which topics are present in the Enron emails. With a quick print of words assigned to the topics, you can do a first exploration about whether there are any obvious topics that jump out. Be mindful that the topic model is heavy to calculate so it will take a while to run. Let's give it a try!

Deze oefening maakt deel uit van de cursus

Fraud Detection in Python

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Oefeninstructies

Build the LDA model from gensim models, by inserting the corpus and dictionary.
Save the 5 topics by running print topics on the model results, and select the top 5 words.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Define the LDA model
ldamodel = gensim.models.____.____(____, num_topics=5, id2word=____, passes=5)

# Save the topics and top 5 words
topics = ____.____(num_words=____)

# Print the results
for topic in topics:
    print(topic)

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