Natural Language Processing Sentiment Analysis and Text Classification

Question 1

What is sentiment analysis in NLP?

Accepted Answer

The task of determining the emotional tone or opinion expressed in a piece of text

Answer

Sentiment analysis classifies text as positive, negative, or neutral (and sometimes finer-grained emotions), enabling automated opinion mining from reviews, tweets, etc.

Question 2

What is aspect-based sentiment analysis (ABSA)?

Accepted Answer

Identifying sentiment expressed toward specific aspects or features of an entity, rather than overall sentiment

Answer

ABSA extracts aspects (e.g., 'battery life', 'screen') from product reviews and assigns a sentiment polarity to each aspect independently.

Question 3

What is the main challenge of detecting sarcasm in sentiment analysis?

Accepted Answer

Sarcasm uses positive language to convey negative sentiment, contradicting surface-level lexical signals

Answer

Sarcasm creates a mismatch between the literal meaning of words and the intended sentiment, making lexicon-based approaches unreliable.

Question 4

What is the Naive Bayes classifier commonly used for in NLP?

Accepted Answer

Text classification tasks such as spam detection and sentiment analysis based on word frequency features

Answer

Naive Bayes applies Bayes' theorem with a conditional independence assumption between features, making it a fast and effective baseline for text classification.

Question 5

What does a confusion matrix reveal about a text classifier?

Accepted Answer

The counts of true positives, true negatives, false positives, and false negatives for each class

Answer

A confusion matrix shows how often the model predicted each class correctly or confused it with another class, enabling diagnosis of specific error types.

Natural Language Processing Practice Test

Natural Language Processing Practice Test

Natural Language Processing Sentiment Analysis and Text Classification