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MLA-C01 Practice Question: A data scientist is preparing text data for…
A data scientist is preparing text data for sentiment analysis. They need to convert the text into numerical features while reducing the impact of common words. Which feature extraction method should they use?
⚠ Common exam trap
MLA-C01 often tests the confusion between CountVectorizer (raw counts) and TF-IDF (weighted counts) — candidates pick D, but only TF-IDF reduces the impact of common words via inverse document frequency.
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
✓
TF-IDF vectorization
TF-IDF (Term Frequency–Inverse Document Frequency) converts text to numerical features while down-weighting common words (like 'the', 'is') that appear across many documents, exactly matching the requirement to reduce the impact of common words. It is the standard feature extraction method for sentiment analysis when common-word influence must be minimized.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Word2Vec embeddings
Why it's wrong here
Word2Vec produces dense contextual embeddings that capture semantic similarity but assign no inverse-document-frequency weighting, so common words are not down-weighted. It is tempting because it is the standard neural text representation, and would be correct when semantic relationships between words matter more than reducing frequent-term influence.
- ✓
TF-IDF vectorization
Why this is correct
TF-IDF vectorization weights each term by its frequency within a document, scaled inversely by how many documents contain it. This downweights common words such as "the" while emphasising distinctive terms, directly satisfying the requirement to reduce the impact of frequent words during numerical feature extraction.
- ✗
Label encoding of each word
Why it's wrong here
Label encoding assigns each distinct word an arbitrary integer, producing ordinal values with no frequency weighting, so common words retain full influence and no document-term matrix is built. It is tempting because it converts text to numbers cheaply, and would suit encoding a fixed set of categorical labels rather than free-text sentiment features.
- ✗
CountVectorizer with n-grams
Why it's wrong here
CountVectorizer with n-grams counts raw term occurrences, so frequent common words dominate the feature matrix unless stop words or TF-IDF weighting are applied separately. It is tempting because it is the standard bag-of-words extraction method, and would be correct when term frequency alone is wanted without down-weighting common words.
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