1. What is sentiment analysis?
A. The process of identifying named entities in a text
B. The task of extracting subjective information from text to determine opinions or emotions
C. The process of converting text to numerical data
D. The technique used to classify text into categories
Answer: B
2. Which of the following is the main goal of sentiment analysis?
A. To categorize text into different topics
B. To determine the overall sentiment or opinion in a piece of text (positive, negative, neutral)
C. To extract keywords from text
D. To translate text into different languages
Answer: B
3. What type of data is typically analyzed in sentiment analysis?
A. Audio files
B. Video content
C. Text data from sources like social media, reviews, and articles
D. Structured data from relational databases
Answer: C
4. Which of the following is a sentiment analysis classification?
A. Positive, Negative, Neutral
B. Document, Sentence, Word
C. Subject, Predicate, Object
D. Verb, Noun, Adjective
Answer: A
5. What is the primary method used for sentiment analysis?
A. Natural Language Processing (NLP)
B. Clustering
C. Regression analysis
D. Database indexing
Answer: A
6. What is a common challenge in sentiment analysis?
A. Analyzing the grammatical structure of a sentence
B. Handling sarcasm and irony in text
C. Classifying text as binary only (positive/negative)
D. Managing large datasets
Answer: B
7. Which of the following can be used in sentiment analysis to detect polarity (positive, negative, neutral)?
A. Bag of Words (BoW) model
B. Named Entity Recognition (NER)
C. Latent Dirichlet Allocation (LDA)
D. Part-of-Speech (POS) tagging
Answer: A
8. Which type of sentiment analysis aims to classify sentiment at a document level?
A. Fine-grained sentiment analysis
B. Sentence-level sentiment analysis
C. Document-level sentiment analysis
D. Aspect-based sentiment analysis
Answer: C
9. What is the term for identifying specific aspects or features in a product or service and analyzing the sentiment about those aspects?
A. Sentiment polarity
B. Aspect-based sentiment analysis
C. Topic modeling
D. Opinion summarization
Answer: B
10. In sentiment analysis, what does “polarity” refer to?
A. The intensity of sentiment in the text
B. The overall topic or theme of the text
C. The emotional tone of the text (positive, negative, or neutral)
D. The grammatical structure of the text
Answer: C
11. Which of the following sentiment analysis techniques can handle ambiguous words or phrases in text?
A. Rule-based approaches
B. Machine learning approaches
C. Lexicon-based approaches
D. All of the above
Answer: D
12. What is a lexicon-based sentiment analysis approach?
A. Analyzing sentiment using predefined sets of words (lexicons) with known sentiment values
B. Training a machine learning model on labeled data
C. Extracting topics from a corpus of text
D. Clustering text into positive, negative, or neutral categories
Answer: A
13. Which of the following is a popular machine learning algorithm used for sentiment analysis?
A. Support Vector Machine (SVM)
B. K-means clustering
C. Random Forest
D. Linear Regression
Answer: A
14. In sentiment analysis, what is the challenge of “sarcasm” typically associated with?
A. Identifying correct polarity
B. Understanding the grammatical structure
C. Interpreting numerical data
D. Detecting stop words
Answer: A
15. Which of the following tools or libraries can be used for performing sentiment analysis?
A. NLTK (Natural Language Toolkit)
B. OpenCV
C. TensorFlow
D. All of the above
Answer: A