Text Mining and Natural Language Processing (NLP) MCQs December 22, 2025November 19, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. What is Text Mining? (A) Extracting patterns from numerical data (B) Analyzing multimedia content (C) Extracting meaningful information and patterns from textual data (D) Optimizing database structures 2. Which of the following is an application of Natural Language Processing (NLP)? (A) Image recognition (B) Data normalization (C) Speech-to-text conversion (D) Video summarization 3. In NLP, what does “tokenization” refer to? (A) Translating text from one language to another (B) Splitting text into smaller parts like words or sentences (C) Removing stop words from the text (D) Converting text into numerical data 4. What is a “stop word” in text mining? (A) Commonly used words (e.g., “is”, “the”) that are often excluded from analysis (B) Words with the highest frequency in a document (C) Words that indicate the end of a sentence (D) Words that cannot be tokenized 5. Which algorithm is commonly used for text classification tasks? (A) K-means clustering (B) Apriori (C) PageRank (D) Naive Bayes 6. Which technique measures the importance of a word in a document relative to a collection of documents? (A) Sentiment analysis (B) TF-IDF (Term Frequency-Inverse Document Frequency) (C) Part-of-speech tagging (D) Named Entity Recognition 7. What is lemmatization in NLP? (A) Reducing a word to its base or root form while considering its context (B) Grouping similar words together (C) Removing punctuation from the text (D) Translating text into another language 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 9. Which of the following is an example of unstructured data? (A) Social media posts (B) Excel spreadsheets (C) Transaction records (D) Relational databases 10. What is the Bag of Words (BoW) model in text mining? (A) A representation of text where each word is treated as a feature and its frequency is recorded (B) A technique for detecting named entities in text (C) A deep learning model for understanding natural language (D) A grammar-based approach to parse sentences 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 12. Which NLP task involves identifying whether a text is positive, negative, or neutral? (A) Sentiment analysis (B) Text summarization (C) Topic modeling (D) Language translation 13. What is stemming in text preprocessing? (A) Reducing words to their base form by chopping off suffixes (e.g., “running” → “run”) (B) Removing stop words from a document (C) Analyzing sentence structure (D) Identifying named entities 14. Which algorithm is commonly used for topic modeling in text mining? (A) Random Forest (B) Support Vector Machines (SVM) (C) Latent Dirichlet Allocation (LDA) (D) Gradient Boosting 15. What is the primary challenge in processing natural language data? (A) Handling the ambiguity and variability of human language (B) Managing structured data (C) Designing efficient SQL queries (D) Cleaning relational data