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WorksheetsText Mining and Analytics Quiz
Total questions: 50
Worksheet time: 25mins
What is Text Mining?
Extracting data from images
Analyzing structured data
Deriving meaningful information from unstructured text
Creating databases from spreadsheets
Which of the following is a key step in the text mining process?
Image recognition
Text preprocessing
Hardware installation
GUI design
Which of the following is NOT a common application of Text Mining?
Sentiment analysis
Topic modeling
Text summarization
Circuit board printing
Which method is commonly used for converting text into numerical form for analysis?
Tokenization
Text-to-Speech
Vectorization
Annotation
What is 'TF-IDF' used for in Text Mining?
Visualizing documents
Encrypting text
Measuring the importance of words in documents
Formatting text for printing
Named Entity Recognition (NER) is used to:
Identify colors in images
Find grammatical errors
Detect names of people, places, and organizations
Create new sentences
Which of the following algorithms is commonly used for topic modeling in text mining?
Naive Bayes
K-Means
LDA (Latent Dirichlet Allocation)
Apriori
One major challenge in text mining is:
Lack of programming languages
High cost of computers
Ambiguity and variability in human language
Inability to store data
Which software/library is widely used for text mining in Python?
NumPy
Pandas
Matplotlib
NLTK
Which technique is used in text mining to reduce words to their root form?
Vectorization
Lemmatization
Parsing
Chunking
What is the role of stop words in text preprocessing?
Words that are important and kept
Words that are removed due to low importance
Words that appear only once
Words that are misspelled
In which of the following fields is text mining not commonly used?
E-commerce
Social media analytics
Genomic DNA sequencing
Legal document analysis
What is a corpus in the context of text mining?
A single document
A collection of documents
A type of algorithm
A grammar rule set
Which method can be used for unsupervised classification of text documents?
Naive Bayes
Decision Trees
LDA (Latent Dirichlet Allocation)
Logistic Regression
Word2Vec is used in text mining for:
Creating word embeddings
Clustering documents
Tokenizing text
Sorting words alphabetically
Which of the following is considered unstructured data?
Excel sheet with sales data
SQL database
Text documents and emails
JSON files
Which task is usually not part of text preprocessing?
Removing punctuation
Lowercasing all words
Adding new words to the text
Removing stop words
What is the goal of classification in text mining?
To translate text
To group similar documents without labels
To assign predefined categories to text
To remove irrelevant data
Which of these is a stop word in most text mining tasks?
Computer
Innovation
The
Analysis
Text summarization aims to:
Translate text into another language
Make text longer
Shorten text while keeping key information
Encrypt text
Which of the following is NOT a tool or library used for text mining?
NLTK
SpaCy
TensorFlow
Adobe Photoshop
What is stemming?
Grouping words by color
Replacing all text with symbols
Reducing words to their base or root form
Adding punctuation
In text mining, which file format is commonly used for input data?
.docx
.txt
All of the above
Which of the following is a common output of text mining?
Audio files
Structured data or insights
Compiled code
Images
Which of the following best defines information extraction in text mining?
Reading documents aloud
Extracting structured information from unstructured text
Formatting text into paragraphs
Translating text into binary
The process of identifying parts of speech (noun, verb, etc.) in a sentence is called:
Lemmatization
Syntax removal
Part-of-Speech (POS) tagging
Tokenization
What is a common use of text mining in customer service?
Reading emails manually
Organizing employee schedules
Analyzing customer feedback or support tickets
Generating invoices
Which of the following is a machine learning approach often used in text classification?
Waterfall model
Naive Bayes
Regression testing
Linear programming
In text mining, what is a feature?
A punctuation mark
A category label
A measurable property or word in the text
A file format
Which term describes how often a word appears in a document?
Term frequency
Inverse frequency
Word ratio
Text density
What is NOT a use of text mining?
Conveniently search for different and useful data.
Rewrite a bad-quality text in a high-quality one
Efficiently search a document, take out similar words, underline repeated words.
Identify and pull out the patterns from a large amount of hidden and unstructured data.
Text mining is the process of (a)_____ from (b)_____(a) extracting information; (b) textual sources(a) organizing sentences; (b) messy sources(a) obtaining numbers; (b) texts(a) building models; (b) structured documents
extracting information
organizing sentences
obtaining numbers
building models
Which one of the following is a perfect statement for Term Frequency (TF)?
% of words taking each document is called ___.
Talking about how popular feature across all the reviews.
To remove the effect of outliner concepts is called ____.
None of the Above.
What are the common NLP techniques?
Named Entity Recognition.
Sentiment Analysis.
Text Modeling.
All the above.
Removing words like "and", "is", "a", "an", "the" from a sentence is called as?
Stemming.
Lemmatization.
Stop word.
Tokenization.
The process of deriving high quality information from text is referred to as ________.
Image Mining.
Database Mining.
Multimedia Mining.
Text Mining.
The various aspects of text mining is/are____________.I. The text and documents are gathered into a corpus and organized. II. The corpus is analyzed for structure. The result is a matrix mapping important terms to source documents. III. The structured data then analyzes forward structures , sequences and frequency
(I), (II) only.
(II),(III) only.
(I), (II) and (III).
None of the above
With text mining we are able to perform _________ tasks.
Text categorization.
Text clustering.
Concept/entity extraction.
All of the above.
With text mining we are able to perform ________ tasks.
Entity relation modeling (i.e., learning relations between named entities).
Sentiment analysis.
Document summarization.
All of the above.
Text mining is _________ method.
Supervised learning.
Unsupervised Learning.
Automated learning.
None of the above.
Machine learning algorithms cannot work with raw text directly; the text must be converted into numbers. Specifically, vectors of numbers. This is called _________.
Feature creation.
Feature coding.
Feature extraction or feature encoding.
None of the above.
Which method helps reduce sparsity in text data representation?
One-hot encoding
Stop-word removal
Tokenization
Punctuation insertion
What does "document frequency" refer to in text mining?
How often a document is read
Number of times a term appears across all documents
The total length of the document
The reading level of a document
Which of these is not a valid step in the text mining pipeline?
Data cleaning
Feature extraction
Audio processing
Model building
In Natural Language Processing, a "chunk" usually refers to:
A synonym for token
A group of words that form a meaningful phrase
A sentence fragment
A random block of text
Which approach helps in reducing different forms of a word to a common base form?
Parsing
Stemming
Chunking
Embedding
Which term best describes the process of converting a collection of documents into structured data for analysis?
Data mining
Text summarization
Text mining
Data warehousing
Which of the following is an example of a token in text mining?
An entire paragraph
A punctuation mark
A single word or term
A document title
Which one is an example of supervised learning in text mining?
Grouping customer complaints into topics
Sentiment analysis using labeled data
Keyword extraction
Text summarization
What kind of data does text mining usually deal with?
Numeric data
Time-series data
Unstructured textual data
Audio data
