NLP Sentiment Analysis 4

Question 1

What role does 'transfer learning' play in modern sentiment analysis?

Accepted Answer

It leverages pretrained language model weights to improve sentiment classification with less labeled data

Answer

Transfer learning fine-tunes pretrained models like BERT on labeled sentiment data, achieving high accuracy with fewer examples.

Question 2

Which sentiment analysis technique uses a graph of words to propagate sentiment scores?

Accepted Answer

Graph-based sentiment propagation

Answer

Graph-based propagation spreads known sentiment scores from seed words through a word co-occurrence or similarity graph to label unlabeled words.

Question 3

What is 'emotion detection' and how does it differ from polarity classification?

Accepted Answer

Emotion detection identifies specific emotions like joy or anger, while polarity only classifies positive or negative

Answer

Polarity classification assigns positive/negative/neutral labels, while emotion detection maps text to discrete emotion categories such as Ekman's six basic emotions.

Question 4

What is the 'Pointwise Mutual Information' (PMI) method used for in sentiment analysis?

Accepted Answer

Estimating semantic orientation of phrases by comparing co-occurrence with positive vs. negative seed words

Answer

PMI measures how much more often a phrase co-occurs with positive seed words versus negative ones to estimate its sentiment orientation.

Question 5

Which data augmentation technique is commonly applied to improve sentiment classifier robustness?

Accepted Answer

Synonym replacement, random insertion, or back-translation to create varied training examples

Answer

Techniques like synonym replacement and back-translation generate new training sentences that preserve sentiment while varying wording.

NLP Practice Test

NLP Practice Test

NLP Sentiment Analysis 4