Sentiment Analysis Flashcards

Which challenge does 'negation handling' address in sentiment analysis?

Answer: Reversing sentiment when words like 'not' or 'never' appear

Negation handling ensures that words like 'not' flip the sentiment of subsequent words (e.g., 'not good' becomes negative).

What is 'aspect-based sentiment analysis' (ABSA)?

Answer: Identifying sentiment toward specific aspects or features within a text

ABSA extracts sentiment at the level of specific aspects (e.g., 'battery life' or 'camera') rather than the whole review.

Which metric is most appropriate for evaluating sentiment analysis on a highly imbalanced dataset?

Answer: F1-score

F1-score balances precision and recall, making it more informative than accuracy when class distribution is skewed.

What does the term 'valence' refer to in sentiment analysis?

Answer: The positive or negative direction of an emotional expression

Valence captures whether an expression is positive or negative, forming the core dimension of sentiment polarity.

Which approach is used in 'distant supervision' for sentiment analysis?

Answer: Automatically labeling data using heuristics such as review star ratings

Distant supervision exploits noisy but abundant labels (like star ratings) to train sentiment classifiers without manual annotation.

What problem does 'domain adaptation' solve in sentiment analysis?

Answer: Improving model performance when training and test data come from different domains

Domain adaptation addresses the drop in performance when a model trained on one domain (e.g., movies) is applied to another (e.g., electronics).

In the context of sentiment lexicons, what is 'SentiWordNet'?

Answer: A lexical resource that assigns positive, negative, and objective scores to WordNet synsets

SentiWordNet extends WordNet by assigning sentiment scores (positivity, negativity, objectivity) to each synset.