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!

本练习是课程的一部分

Fraud Detection in Python

查看课程

练习说明

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.

交互式实操练习

通过完成这段示例代码来试试这个练习。

# 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)

编辑并运行代码