MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is working with a dataset containing text reviews. The goal is to classify sentiment. During EDA, they compute the word frequency distribution. They notice that the most frequent words are common stop words like 'the', 'and', 'a'. Which action should they take to improve the feature representation for modeling?
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
✓
Remove the stop words from the text before creating the bag-of-words representation.
The correct action is to remove stop words (option C) because stop words like 'the', 'and', 'a' are common across all documents and do not carry sentiment information. Removing them allows the model to focus on content words that are more indicative of sentiment. Option A (n-grams) captures phrase patterns but still includes stop words, so it does not address the issue. Option B (adding more stop words) would remove even more words, potentially including some useful for sentiment, making it less effective than using a standard stop word list. Option D (stemming) reduces words to root forms but does not remove stop words, so it does not solve the problem of high-frequency stop words dominating the feature space.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use n-grams instead of unigrams to capture phrase patterns.
Why it's wrong here
N-grams include stop words in phrases, still noisy.
- ✗
Add more stop words to the default list to remove even more common words.
Why it's wrong here
Removing too many words may discard sentiment-bearing words like 'not'.
- ✓
Remove the stop words from the text before creating the bag-of-words representation.
Why this is correct
Stop words are usually not informative for sentiment; removing them reduces noise.
- ✗
Apply stemming to reduce words to their root forms.
Why it's wrong here
Stemming does not remove stop words.
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