Sentiment Analysis Flashcards
7 cards from real NLP practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Sentiment Analysis flashcards as text
What is 'subjectivity detection' as a preprocessing step in sentiment analysis?
Answer: Distinguishing objective factual statements from subjective opinionated text
Subjectivity detection filters out objective sentences so sentiment analysis focuses only on opinion-bearing content.
Which technique helps capture long-range dependencies in sentiment classification using deep learning?
Answer: LSTM or Transformer-based model
LSTMs and Transformers maintain context across long sequences, capturing dependencies that bag-of-words models miss.
What does 'opinion mining' typically extract beyond overall sentiment polarity?
Answer: Opinion holders, opinion targets, and sentiment expressions
Opinion mining identifies who holds the opinion, what the opinion is about, and the expressed sentiment toward it.
How does 'contextualized word embedding' (e.g., BERT) improve sentiment analysis over static embeddings?
Answer: It generates different vector representations for the same word depending on its context
Contextualized embeddings like BERT encode surrounding context, so 'great' in a positive vs. sarcastic sentence gets different representations.
Which strategy is commonly used to handle sarcasm detection in sentiment analysis?
Answer: Incorporating incongruity signals between sentiment and context or using multimodal cues
Sarcasm often involves contrast between literal positive words and negative context, so detecting incongruity is a key strategy.
What is the primary limitation of lexicon-based sentiment analysis?
Answer: It struggles with domain-specific language and context-dependent word meanings
Lexicon-based methods rely on fixed word-sentiment mappings that fail when domain context shifts the meaning (e.g., 'sick' meaning 'cool' in slang).
In multi-class sentiment analysis, what does 'fine-grained' classification refer to?
Answer: Classifying sentiment into more than three categories such as very positive, positive, neutral, negative, very negative
Fine-grained sentiment goes beyond positive/negative/neutral to capture a spectrum of intensity levels.