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MLA-C01 Practice Question: A team is preparing text data for sentiment…
A team is preparing text data for sentiment analysis. They have a large corpus of customer reviews. They want to convert the text into numerical features using a technique that captures word importance relative to the whole corpus. Which feature extraction method should they use?
⚠ Common exam trap
MLA-C01 often tests the distinction between frequency-based methods (CountVectorizer, TF-IDF) and embedding-based methods (Word2Vec) — candidates pick Word2Vec for 'importance' when the question specifically asks for corpus-relative term weighting.
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
TF-IDF (Term Frequency–Inverse Document Frequency) weights each word by how often it appears in a document relative to how often it appears across the entire corpus, so common words like 'the' are down-weighted while distinctive words are up-weighted. This directly captures word importance relative to the whole corpus, which is exactly what the team needs for sentiment analysis feature extraction.
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 semantic embeddings from local context windows, not corpus-relative importance weights, so it does not satisfy the requirement to capture word importance across the whole corpus. It is tempting because embeddings capture meaning, but TF-IDF is the correct choice when term weighting by document frequency is required.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding assigns each token a binary column with no weighting, so it cannot express a word's importance relative to the corpus and produces sparse, high-dimensional vectors. It is tempting for small vocabularies or categorical labels, but TF-IDF is the method that scales term frequency by inverse document frequency across the corpus.
- ✓
TF-IDF
Why this is correct
TF-IDF weights each term by its frequency within a review against its rarity across the whole corpus, so words that distinguish individual reviews score higher than common ones. That corpus-relative importance weighting is exactly what the scenario requires.
- ✗
Bag-of-words (CountVectorizer)
Why it's wrong here
CountVectorizer records raw term frequencies without inverse document frequency, so common words across the corpus are not down-weighted and importance relative to the whole corpus is not captured. It is tempting as a simple baseline, but TF-IDF applies the IDF weighting the scenario explicitly requires.
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