Tutorial 1b

Question 1

What is the purpose of cleaning text data?

Accepted Answer

To remove “noise” (irrelevant information) and standardize the text

Question 2

Why is it useful to remove symbols (like “$” or “&”) in text processing?

Accepted Answer

Symbols might not add value in word-based analysis

Question 3

Which of the following are typically removed during text processing?

Accepted Answer

Stopwords, punctuation, numbers, emoticons, emojis, and extra spaces

Question 4

What is the purpose of removing HTML tags (e.g.

Accepted Answer

To make the text more readable and remove formatting code (like
)

Question 5

Why is word-based analysis important in text processing, and what can it help us achieve?

Accepted Answer

To identify frequently used words, sentiments and themes

Question 6

What is data processing?

Accepted Answer

Cleaning and organizing raw data

Question 7

Why do we remove extra white spaces in text processing?

Accepted Answer

To improve readability and avoid unnecessary spaces.

Question 8

If you have the date "03/07/2023" and it’s unclear whether it represents March 7th or July 3rd, what should you do?

Accepted Answer

Convert the date to a standard date format, like "YYYY-MM-DD."

Question 9

Why do we remove slang (like “lol” or “idk”) in text processing?

Accepted Answer

To standardize language, slang terms can vary in meaning

Question 10

What are "stop words"?

Accepted Answer

Common words that add little meaning (like "the", "but", "a", "an", etc)

Question 11

Why do we remove punctuation from text?

Accepted Answer

punctuation doesn’t add much value in word-based analysis

Question 12

If we remove stop words from the sentence "Data science is fun and informative," what would the result be?

Accepted Answer

"Data science fun informative"

Question 13

What is the purpose of the mutate function (dplyr package) in data processing?

Accepted Answer

To add or modify columns in a data frame

Question 14

Why do we remove emojis and emoticons in data processing?

Accepted Answer

To standardize the text and remove symbols

Question 15

In text processing, why do we focus on "meaningful words" and remove less important words like stopwords?

Accepted Answer

To focus on nouns, verbs, and adjectives words that carry more meaning

Question 16

Why do we convert text to lowercase when processing text?

Accepted Answer

To standardize text for analysis, "Hello" and "hello" are treated the same

Question 17

If you apply tokenization to the sentence "R is amazing for data analysis," what would the tokens be?

Accepted Answer

["R", "is", "amazing", "for", "data", "analysis"]

Question 18

Why do we replace contractions (like “isn’t” or “can’t”) with full words in the context of text processing?

Accepted Answer

To ensure each word is standardized for analysis

Question 19

What does “tokenizing” mean in text processing?

Accepted Answer

Breaking text into individual words or phrases

Question 20

What is the main purpose of text processing in data analysis?

Accepted Answer

To make text data consistent (standardized) and easier to analyze

Questions 20