Recall & Review
beginner
Why is text data different from numerical data in analysis?
Text data is unstructured and made of words, not numbers. It needs special steps to turn it into numbers before analysis.
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beginner
What does 'tokenization' mean in text data processing?
Tokenization means breaking text into small pieces like words or sentences to analyze them easily.
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beginner
Why do we remove stop words in text analysis?
Stop words like 'the' or 'and' appear a lot but add little meaning, so removing them helps focus on important words.
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intermediate
What is 'vectorization' in handling text data?
Vectorization converts text into numbers (vectors) so computers can understand and analyze it.
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intermediate
Why is handling text data more complex than numbers?
Text has many forms, meanings, and context. It needs cleaning, transforming, and understanding before analysis.
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What is the first step in preparing text data for analysis?
✗ Incorrect
Tokenization breaks text into smaller parts like words, which is usually the first step.
Why do we convert text into vectors?
✗ Incorrect
Computers need numbers to do math, so text is converted into vectors for analysis.
Which of these is NOT a reason text data needs special handling?
✗ Incorrect
Text is not numeric by nature; this is why it needs special handling.
What are stop words?
✗ Incorrect
Stop words are common words like 'the' or 'is' that add little meaning.
Which process helps reduce different forms of a word to a base form?
✗ Incorrect
Stemming or lemmatization reduces words to their root form for easier analysis.
Explain why text data requires special handling before analysis.
Think about how text is different from numbers and what steps help computers understand it.
You got /5 concepts.
Describe the main steps involved in preparing text data for analysis.
Consider the process from raw text to numbers.
You got /5 concepts.