NLP Sentiment Analysis 5

Question 1

What is the 'semantic orientation' of a word in sentiment analysis?

Accepted Answer

The degree to which a word is associated with positive or negative sentiment

Answer

Semantic orientation quantifies how strongly a word skews positive or negative based on its associations and usage patterns.

Question 2

Which issue arises with 'word-level' sentiment analysis that 'sentence-level' analysis helps mitigate?

Accepted Answer

Missing context that determines whether a word is used positively or negatively in the sentence

Answer

At word level, 'unpredictable' looks negative, but sentence context like 'unpredictably funny' reveals a positive sentiment.

Question 3

What is 'sentiment composition' in the context of phrase-level analysis?

Accepted Answer

Computing the sentiment of a phrase by composing sentiments of its constituent words using linguistic rules

Answer

Sentiment composition uses rules (e.g., modifier + adjective) to derive phrase-level sentiment from word-level scores and syntactic structure.

Question 4

In the context of Twitter/social media sentiment analysis, what makes the task particularly challenging?

Accepted Answer

Informal language, abbreviations, emojis, sarcasm, and very short text with little context

Answer

Social media text is noisy with abbreviations, hashtags, emojis, and sarcasm, all of which break assumptions of standard NLP models.

Question 5

What does a 'Recursive Neural Network' (RecNN) exploit for sentiment analysis?

Accepted Answer

The parse tree (syntactic structure) of a sentence to compose sentiment bottom-up

Answer

RecNNs apply composition functions at each node of a parse tree, building phrase-level sentiment representations recursively from leaf words.

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