You want to find 3 topics from a set of news articles using LDA with scikit-learn. After fitting the model, how do you find the top 3 words that represent each topic?

hard📝 Application Q15 of 15

NLP - Topic Modeling

AUse CountVectorizer's get_feature_names_out to get top words directly

BUse lda.transform to get topic distribution, then select words with highest probabilities

CUse lda.components_ to get word weights, then map top indices to feature names from CountVectorizer

DUse lda.fit_transform output and pick first 3 words from each document

Step-by-Step Solution

Solution:

Step 1: Understand lda.components_ role
lda.components_ contains the importance (weights) of each word for every topic.
Step 2: Map top weights to words
Use CountVectorizer's get_feature_names_out to get the vocabulary, then select top 3 words per topic by sorting weights.
Final Answer:
Use lda.components_ to get word weights, then map top indices to feature names from CountVectorizer -> Option C
Quick Check:
Top words = components_ + feature names [OK]

Quick Trick: Top words per topic come from components_ and vectorizer vocab [OK]

Common Mistakes:

MISTAKES

Using transform output to find top words
Assuming vectorizer alone gives topic words
Picking words directly from documents without weights

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More NLP Quizzes

You want to find 3 topics from a set of news articles using LDA with scikit-learn. After fitting the model, how do you find the top 3 words that represent each topic?

Step 1: Understand lda.components_ role

Step 2: Map top weights to words

Final Answer:

Quick Check:

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