MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset of customer reviews. The dataset contains a text column 'review' and a numerical rating from 1 to 5. The data scientist wants to create features for sentiment analysis. Which THREE preprocessing steps should be applied to the text data before feature extraction? (Choose THREE.)
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between preprocessing steps that are specific to text (like stemming, lowercasing, stop word removal) versus those meant for numerical data (like normalization), and candidates may mistakenly apply scaling techniques to text or forget that tokenization is a prerequisite but not always listed as a separate 'correct' step in multi-select questions.
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
✓
Apply stemming to reduce words to their root form.
Stemming reduces words to their root form (e.g., 'running' to 'run'), which consolidates variations of the same word and reduces feature dimensionality. This is a standard preprocessing step before feature extraction in NLP tasks like sentiment analysis, as it helps the model generalize across different word forms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Standardize the text data using z-score normalization.
Why it's wrong here
Text data is categorical; standardization is not applicable.
- ✓
Apply stemming to reduce words to their root form.
Why this is correct
Stemming groups related words, reducing feature dimensionality.
- ✗
Tokenize the text into individual words.
Why it's wrong here
Tokenization is a prerequisite but not typically considered a preprocessing step after tokenization; it is part of the pipeline.
- ✓
Convert all text to lowercase.
Why this is correct
Lowercasing ensures consistency and reduces vocabulary size.
- ✓
Remove common stop words (e.g., 'the', 'and', 'is').
Why this is correct
Stop words are frequent but often irrelevant for sentiment analysis.
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