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Data Analysis Foundations with Python

Quiz Part VII: Case Studies

Chapter 17: Case Study 2 - Social Media Sentiment Analysis

  1. What is tokenization in the context of text preprocessing? 
    • A) Converting text to upper case
    • B) Breaking text into individual words or sub-words
    • C) Removing special characters from text
    • D) Translating text into another language
  2. Which algorithm is commonly used for sentiment analysis?
    • A) Linear Regression
    • B) Naive Bayes
    • C) K-Nearest Neighbors
    • D) ARIMA
  3. Why is data collection important in social media sentiment analysis?
    • A) To have sufficient data for training
    • B) To better understand the audience
    • C) To identify key features for model training
    • D) All of the above

Chapter 17: Case Study 2 - Social Media Sentiment Analysis

  1. What is tokenization in the context of text preprocessing? 
    • A) Converting text to upper case
    • B) Breaking text into individual words or sub-words
    • C) Removing special characters from text
    • D) Translating text into another language
  2. Which algorithm is commonly used for sentiment analysis?
    • A) Linear Regression
    • B) Naive Bayes
    • C) K-Nearest Neighbors
    • D) ARIMA
  3. Why is data collection important in social media sentiment analysis?
    • A) To have sufficient data for training
    • B) To better understand the audience
    • C) To identify key features for model training
    • D) All of the above

Chapter 17: Case Study 2 - Social Media Sentiment Analysis

  1. What is tokenization in the context of text preprocessing? 
    • A) Converting text to upper case
    • B) Breaking text into individual words or sub-words
    • C) Removing special characters from text
    • D) Translating text into another language
  2. Which algorithm is commonly used for sentiment analysis?
    • A) Linear Regression
    • B) Naive Bayes
    • C) K-Nearest Neighbors
    • D) ARIMA
  3. Why is data collection important in social media sentiment analysis?
    • A) To have sufficient data for training
    • B) To better understand the audience
    • C) To identify key features for model training
    • D) All of the above

Chapter 17: Case Study 2 - Social Media Sentiment Analysis

  1. What is tokenization in the context of text preprocessing? 
    • A) Converting text to upper case
    • B) Breaking text into individual words or sub-words
    • C) Removing special characters from text
    • D) Translating text into another language
  2. Which algorithm is commonly used for sentiment analysis?
    • A) Linear Regression
    • B) Naive Bayes
    • C) K-Nearest Neighbors
    • D) ARIMA
  3. Why is data collection important in social media sentiment analysis?
    • A) To have sufficient data for training
    • B) To better understand the audience
    • C) To identify key features for model training
    • D) All of the above