1. What is Text Mining?
A. Extracting patterns from numerical data
B. Extracting meaningful information and patterns from textual data
C. Analyzing multimedia content
D. Optimizing database structures
Answer: B
2. Which of the following is an application of Natural Language Processing (NLP)?
A. Image recognition
B. Speech-to-text conversion
C. Data normalization
D. Video summarization
Answer: B
3. In NLP, what does “tokenization” refer to?
A. Splitting text into smaller parts like words or sentences
B. Translating text from one language to another
C. Removing stop words from the text
D. Converting text into numerical data
Answer: A
4. What is a “stop word” in text mining?
A. Words with the highest frequency in a document
B. Commonly used words (e.g., “is”, “the”) that are often excluded from analysis
C. Words that indicate the end of a sentence
D. Words that cannot be tokenized
Answer: B
5. Which algorithm is commonly used for text classification tasks?
A. K-means clustering
B. Naive Bayes
C. PageRank
D. Apriori
Answer: B
6. Which of the following techniques is used to measure the importance of a word in a document relative to a collection of documents?
A. Sentiment analysis
B. Part-of-speech tagging
C. TF-IDF (Term Frequency-Inverse Document Frequency)
D. Named Entity Recognition
Answer: C
7. What is lemmatization in NLP?
A. Grouping similar words together
B. Reducing a word to its base or root form while considering its context
C. Removing punctuation from the text
D. Translating text into another language
Answer: B
8. What does Named Entity Recognition (NER) aim to identify in text?
A. Emotions or sentiments
B. Specific entities like names, locations, dates, and organizations
C. Commonly used stop words
D. The structure of a sentence
Answer: B
9. Which of the following is an example of unstructured data?
A. Transaction records
B. Excel spreadsheets
C. Social media posts
D. Relational databases
Answer: C
10. What is the Bag of Words (BoW) model in text mining?
A. A technique for detecting named entities in text
B. A representation of text where each word is treated as a feature and its frequency is recorded
C. A deep learning model for understanding natural language
D. A grammar-based approach to parse sentences
Answer: B
11. What is a word embedding in NLP?
A. A method to convert text into numerical vectors while preserving semantic meaning
B. A process to remove redundant words from a text
C. A technique to group words into categories
D. A tool for tokenizing text
Answer: A
12. Which NLP task involves identifying whether a given piece of text is positive, negative, or neutral?
A. Text summarization
B. Sentiment analysis
C. Topic modeling
D. Language translation
Answer: B
13. What is stemming in text preprocessing?
A. Removing stop words from a document
B. Reducing words to their base form by chopping off suffixes (e.g., “running” → “run”)
C. Analyzing sentence structure
D. Identifying named entities
Answer: B
14. Which algorithm is commonly used for topic modeling in text mining?
A. Latent Dirichlet Allocation (LDA)
B. Support Vector Machines (SVM)
C. Random Forest
D. Gradient Boosting
Answer: A
15. What is the primary challenge in processing natural language data?
A. Managing structured data
B. Handling the ambiguity and variability of human language
C. Designing efficient SQL queries
D. Cleaning relational data
Answer: B