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 the 'semantic orientation' of a word in sentiment analysis?
Answer: The degree to which a word is associated with positive or negative sentiment
Semantic orientation quantifies how strongly a word skews positive or negative based on its associations and usage patterns.
Which issue arises with 'word-level' sentiment analysis that 'sentence-level' analysis helps mitigate?
Answer: Missing context that determines whether a word is used positively or negatively in the sentence
At word level, 'unpredictable' looks negative, but sentence context like 'unpredictably funny' reveals a positive sentiment.
What is 'sentiment composition' in the context of phrase-level analysis?
Answer: Computing the sentiment of a phrase by composing sentiments of its constituent words using linguistic rules
Sentiment composition uses rules (e.g., modifier + adjective) to derive phrase-level sentiment from word-level scores and syntactic structure.
In the context of Twitter/social media sentiment analysis, what makes the task particularly challenging?
Answer: Informal language, abbreviations, emojis, sarcasm, and very short text with little context
Social media text is noisy with abbreviations, hashtags, emojis, and sarcasm, all of which break assumptions of standard NLP models.
What does a 'Recursive Neural Network' (RecNN) exploit for sentiment analysis?
Answer: The parse tree (syntactic structure) of a sentence to compose sentiment bottom-up
RecNNs apply composition functions at each node of a parse tree, building phrase-level sentiment representations recursively from leaf words.
Which evaluation benchmark is widely used for fine-grained sentiment analysis of movie reviews?
Answer: Stanford Sentiment Treebank (SST)
SST provides phrase-level sentiment annotations on parse trees of movie reviews, supporting both binary and fine-grained 5-class evaluation.
What is the purpose of 'sentiment-aware pre-training' in NLP models?
Answer: Incorporating sentiment signals into the pretraining objective to produce embeddings more aligned with sentiment tasks
Sentiment-aware pretraining adds objectives (e.g., predicting review ratings) during pretraining so learned representations better capture affective content.