1. What is the primary objective of sentiment analysis in data mining?
a) To classify text into predefined categories
b) To predict numerical outcomes from text data
c) To analyze emotions expressed in text
d) To summarize large text documents
Answer: c) To analyze emotions expressed in text
2. Which type of sentiment analysis focuses on classifying the sentiment polarity (positive, negative, neutral) of text?
a) Aspect-based sentiment analysis
b) Fine-grained sentiment analysis
c) Emotion detection
d) Opinion mining
Answer: b) Fine-grained sentiment analysis
3. What is the process of tokenizing text data in the context of sentiment analysis?
a) Removing stop words from text
b) Converting words to their base forms
c) Splitting text into individual words or tokens
d) Identifying the main topic of a document
Answer: c) Splitting text into individual words or tokens
4. Which machine learning approach is commonly used for sentiment analysis?
a) Decision trees
b) Linear regression
c) Support Vector Machines (SVM)
d) Association rule mining
Answer: c) Support Vector Machines (SVM)
5. What is the purpose of feature extraction in sentiment analysis?
a) To convert text into numerical vectors
b) To remove noise and outliers from text
c) To normalize the text data
d) To classify text into predefined categories
Answer: a) To convert text into numerical vectors
6. Which of the following is NOT a common sentiment analysis technique?
a) Bag-of-words
b) TF-IDF (Term Frequency-Inverse Document Frequency)
c) Principal Component Analysis (PCA)
d) Word embeddings
Answer: c) Principal Component Analysis (PCA)
7. What is the purpose of sentiment lexicons in sentiment analysis?
a) To visualize sentiment distributions
b) To classify text based on word frequencies
c) To identify sentiment polarity of words
d) To cluster similar sentiments
Answer: c) To identify sentiment polarity of words
8. Which evaluation metric is commonly used to measure the performance of sentiment analysis models?
a) Mean squared error (MSE)
b) Accuracy
c) F1-score
d) R-squared
Answer: c) F1-score
9. What is the role of sentiment analysis APIs in applications?
a) To preprocess text data
b) To visualize sentiment analysis results
c) To perform sentiment analysis automatically using pre-trained models
d) To clean text data
Answer: c) To perform sentiment analysis automatically using pre-trained models
10. Which aspect of sentiment analysis is challenging due to the nuances in human language?
a) Text preprocessing
b) Feature extraction
c) Handling sarcasm and irony
d) Model evaluation
Answer: c) Handling sarcasm and irony
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