20 Sentiment Analysis Quiz Questions and Answers

Question 1: What is the primary goal of sentiment analysis?
A) To translate text into different languages
B) To determine the emotional tone of text
C) To count the frequency of words in a document
D) To correct grammatical errors in sentences

Answer: B
Explanation: Sentiment analysis aims to identify and extract subjective information such as opinions, attitudes, and emotions from text, helping to classify it as positive, negative, or neutral.

Question 2: Which technique uses a predefined list of words with associated sentiment scores?
A) Machine learning-based approach
B) Lexicon-based approach
C) Rule-based approach
D) Deep learning approach

Answer: B
Explanation: A lexicon-based approach relies on sentiment dictionaries that assign scores to words, allowing for quick analysis without training data.

Question 3: In sentiment analysis, what does a neutral sentiment indicate?
A) Strong positive emotions
B) No clear positive or negative emotion
C) Overwhelming negative feelings
D) Mixed emotions only

Answer: B
Explanation: Neutral sentiment means the text lacks a strong emotional bias, often indicating factual or objective content.

Question 4: Which of the following is a common application of sentiment analysis?
A) Weather forecasting
B) Social media monitoring
C) Image processing
D) Network security

Answer: B
Explanation: Sentiment analysis is widely used for social media monitoring to gauge public opinion and track brand sentiment.

Question 5: What challenge does sentiment analysis face with sarcasm?
A) It is always positive
B) Sarcasm can invert the intended meaning
C) It requires no context
D) Sarcasm is easy to detect algorithmically

Answer: B
Explanation: Sarcasm often expresses the opposite of what is literally said, making it difficult for sentiment analysis models to accurately interpret without contextual understanding.

Question 6: Which library is commonly used for sentiment analysis in Python?
A) Pandas
B) TextBlob
C) NumPy
D) Matplotlib

Answer: B
Explanation: TextBlob provides simple tools for sentiment analysis, including polarity and subjectivity scores, making it user-friendly for beginners.

Question 7: What is polarity in the context of sentiment analysis?
A) The grammatical structure of a sentence
B) A measure of positive or negative sentiment
C) The length of the text analyzed
D) The number of unique words

Answer: B
Explanation: Polarity quantifies the sentiment on a scale, typically from negative to positive, to indicate the overall emotional direction.

Question 8: How does supervised learning apply to sentiment analysis?
A) By using unlabeled data only
B) By training models on labeled datasets
C) By ignoring training data entirely
D) By focusing on unsupervised clustering

Answer: B
Explanation: Supervised learning in sentiment analysis involves training models with labeled examples (e.g., texts marked as positive or negative) to predict sentiments in new data.

Question 9: What type of data is most commonly analyzed in sentiment analysis?
A) Numerical data
B) Textual data
C) Audio data
D) Video data

Answer: B
Explanation: Sentiment analysis primarily processes textual data, such as reviews, tweets, or comments, to extract emotional content.

Question 10: Which metric is used to evaluate the accuracy of a sentiment analysis model?
A) Speed of processing
B) F1 score
C) File size
D) Color contrast

Answer: B
Explanation: The F1 score balances precision and recall, providing a reliable measure of a model’s performance in classifying sentiments correctly.

Question 11: In VADER sentiment analysis, what does the compound score represent?
A) A raw count of positive words
B) A normalized score ranging from -1 to +1
C) The total number of sentences
D) The average word length

Answer: B
Explanation: VADER’s compound score aggregates valence scores into a single value between -1 (negative) and +1 (positive) for overall sentiment.

Question 12: Why is context important in sentiment analysis?
A) It has no impact on results
B) It helps disambiguate words with multiple meanings
C) Context only affects positive sentiments
D) It slows down the process unnecessarily

Answer: B
Explanation: Context provides necessary background to interpret ambiguous language, ensuring more accurate sentiment classification.

Question 13: What is aspect-based sentiment analysis?
A) Analyzing overall document sentiment
B) Focusing on sentiments toward specific aspects of a product
C) Ignoring individual words
D) Summarizing text length

Answer: B
Explanation: Aspect-based sentiment analysis breaks down opinions into specific features (e.g., battery life in a phone review) for detailed insights.

Question 14: Which algorithm is often used in machine learning for sentiment analysis?
A) Linear regression
B) Naive Bayes
C) K-means clustering
D) Decision trees only

Answer: B
Explanation: Naive Bayes is effective for text classification tasks like sentiment analysis due to its simplicity and ability to handle high-dimensional data.

Question 15: How does emoji usage affect sentiment analysis?
A) Emojis are ignored in analysis
B) They can enhance or alter the sentiment of text
C) Emojis only add neutral value
D) They complicate grammar but not sentiment

Answer: B
Explanation: Emojis carry emotional weight and can modify the sentiment of accompanying text, making them important for accurate analysis.

Question 16: What is the difference between subjectivity and polarity in sentiment analysis?
A) Subjectivity measures opinion strength, while polarity measures direction
B) They are the same thing
C) Polarity ignores opinions
D) Subjectivity is only for neutral text

Answer: A
Explanation: Subjectivity assesses how opinionated the text is, whereas polarity determines if it’s positive, negative, or neutral.

Question 17: In real-time sentiment analysis, what is a key requirement?
A) High processing speed
B) Manual intervention
C) Low accuracy
D) Static data only

Answer: A
Explanation: Real-time analysis demands quick processing to handle live data streams, such as social media feeds, without delays.

Question 18: Which factor can lead to biased results in sentiment analysis?
A) Using balanced datasets
B) Imbalanced training data
C) Perfectly neutral text
D) Short sentences only

Answer: B
Explanation: Imbalanced datasets, where one sentiment class dominates, can skew model predictions and reduce overall accuracy.

Question 19: What role does tokenization play in sentiment analysis?
A) It encrypts the text
B) It breaks text into smaller units like words for analysis
C) It translates the text
D) It deletes unnecessary words

Answer: B
Explanation: Tokenization preprocesses text by dividing it into tokens (e.g., words), which is essential for feature extraction in sentiment models.

Question 20: How can sentiment analysis be applied in business?
A) To predict stock prices
B) To analyze customer feedback for improvements
C) To design website layouts
D) To manage employee schedules

Answer: B
Explanation: Businesses use sentiment analysis on customer reviews and feedback to identify strengths, weaknesses, and areas for product enhancement.