Text Classification QUIZ (MCQ QUESTIONS AND ANSWERS)

A word with special characters

A word with a high term frequency

A common word that is typically removed from text

A word used in image recognition

A document's class label

A technique for weighting the importance of terms in a document

A type of punctuation mark

A measure of sentiment polarity

F1-score

Accuracy

Font size

Pixels per inch

To remove all punctuation marks

To assign relevant tags to content

To convert text to binary code

To increase the document's length

K-means clustering

Latent Dirichlet Allocation (LDA)

Hierarchical clustering

Label propagation

To increase the number of categories

To organize categories into a tree structure

To remove stopwords from text

To convert text to binary code

An incorrect prediction that a document belongs to the positive class

An incorrect prediction that a document belongs to the negative class

An accurate prediction that a document belongs to the positive class

An accurate prediction that a document belongs to the negative class

The length of a document

The font size of the text

The number of trees in a random forest model

The size of the training dataset

Ignoring the minority class

Oversampling the minority class

Removing the majority class

Converting text to binary code

Assigning labels to documents with no labels

Reducing text dimensionality

Removing stopwords from text

Making text binary

Binary classification

Multi-label classification

Topic classification

Sentiment analysis

To train the model

To make predictions on new data

To evaluate the model's performance and reduce overfitting

To convert text to binary code

Sentiment analysis

Document categorization

Image recognition

Spam detection

A superstition related to text analysis

A challenge of high-dimensional feature spaces

A measure of text length

A measure of sentiment polarity

Identifying key terms in a document

Assigning categories to documents for efficient retrieval

Determining the document's length

Removing all punctuation marks from a document

Converting text to binary code

Removing all punctuation marks

Counting word occurrences in documents

Reducing text dimensionality

Pixels per inch

Accuracy

Temperature in degrees Celsius

Barometric pressure

A collection of physical documents

A model that captures word order in text

A technique for representing text as a collection of words, ignoring word order

A type of punctuation mark

A type of punctuation mark

A machine learning model that assigns labels to text

A type of font

A word in a document

Physical paper documents

Digital files

Sentences

Emails

Converting text to binary code

Assigning categories or labels to text based on its content

Removing all punctuation marks

Splitting text into individual words

Binary classification

Multi-label classification

Sentiment analysis

Document categorization

To evaluate the model's performance

To make predictions on new text

To assign labels to text

To train the model to learn patterns

A document's class label

A unique word in the text

The length of a document

The text's font size

Sentiment analysis

Document categorization

Image recognition

Spam detection

Assigning multiple labels to text

Assigning only one label to text

Splitting text into multiple tokens

Converting text to binary code

Negative emotions

Neutral emotions

Positive emotions

Indifference

Image recognition

Removing stopwords

Stemming

Tokenization

Linear regression

Decision trees

Naïve Bayes

Principal component analysis

"Spam"

"Inbox"

"Junk"

"Ham"