Question 34 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing exploratory data analysis on text data. They want to identify the most common terms and their frequencies. Which approach should they use?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a bag-of-words matrix and compute term frequencies.
A bag-of-words matrix counts the frequency of each term in the text, directly providing the most common terms and their frequencies. Option A is incorrect because sentiment analysis determines the emotional tone of text, not term frequencies. Option B is incorrect because Latent Dirichlet Allocation (LDA) is a topic modeling technique that assigns topics to documents, not term frequencies. Option D is incorrect because word2vec generates dense vector embeddings that capture semantic relationships, not raw term frequencies.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Perform sentiment analysis on the text.
Why it's wrong here
Sentiment analysis gauges polarity, not term frequency.
- ✗
Apply Latent Dirichlet Allocation (LDA) to extract topics.
Why it's wrong here
LDA is for topic modeling, not term frequency.
- ✓
Create a bag-of-words matrix and compute term frequencies.
Why this is correct
Bag-of-words directly provides term counts.
- ✗
Use word2vec to generate word embeddings.
Why it's wrong here
Word2vec captures semantic similarity, not frequencies.
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Last reviewed: Jun 20, 2026
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