Natural Language Processing Sentiment Analysis and Text Classification 2

Question 1

What is a lexicon-based approach to sentiment analysis?

Accepted Answer

Using a pre-built dictionary of words labeled with their sentiment polarity scores to calculate overall text sentiment

Answer

Lexicon-based methods (e.g., VADER, SentiWordNet) assign sentiment scores from a curated word list and aggregate them to determine document-level sentiment without training data.

Question 2

What is multi-label text classification?

Accepted Answer

Assigning multiple non-mutually exclusive labels to a single text, such as tagging a news article with several topics simultaneously

Answer

Multi-label classification allows a document to belong to multiple categories at once (e.g., an article tagged as both 'politics' and 'economy').

Question 3

What is opinion mining?

Accepted Answer

Extracting subjective information such as opinions, evaluations, and emotions from text at scale

Answer

Opinion mining (often used interchangeably with sentiment analysis) encompasses identifying opinion holders, targets, and polarity from large collections of user-generated content.

Question 4

How does class imbalance affect sentiment classification model training?

Accepted Answer

A model trained on imbalanced data tends to be biased toward the majority class and performs poorly on minority classes

Answer

When one sentiment class dominates, the model can achieve high accuracy by always predicting the majority class, so techniques like oversampling or weighted loss are needed.

Question 5

What is the SQuAD dataset commonly used for in NLP?

Accepted Answer

Benchmarking extractive reading comprehension and question answering models

Answer

The Stanford Question Answering Dataset (SQuAD) contains crowd-sourced question-answer pairs where answers are spans from Wikipedia passages.

Natural Language Processing Practice Test

Natural Language Processing Practice Test

Natural Language Processing Sentiment Analysis and Text Classification 2