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Sarcasm

As explained in Starting Data Analytics with Generative AI and Python

An indirect or ironic form of expression in which the intended attitude differs from the literal wording, often conveying criticism or frustration.

Sarcasm uses wording whose intended attitude differs from its literal meaning, making simple sentiment rules unreliable. Context and tone help reveal the criticism or frustration underneath. section 6.2

A misleadingly positive review

“Great, another software update that fixes nothing.” Keyword or dictionary methods may label this positive because of “great” and “fixes,” while its sarcastic context signals frustration. “Back to the drawing board” similarly implies failure and starting over without explicit negative keywords. section 6.2

More capable models look beyond isolated words:

  • Identify positive language used in negative situations and phrases that violate ordinary conversational expectations. section 6.2
  • Use surrounding text and speaker-listener relationships, while recognizing that predictions remain imperfect. section 6.2

How Starting Data Analytics with Generative AI and Python covers it

How deep it goes
Explains linguistic cues, context, relationships, and idioms, but stops short of detailing model architectures or implementation.
Examples
Uses a sarcastic software-update review and the idiom “back to the drawing board” as classification tests.
What it assumes
Assumes familiarity with basic sentiment analysis and contrasts rule-based, dictionary-based, and pretrained approaches.
What you can do after
Evaluate sentiment predictions by checking literal wording against sarcasm cues, idiomatic meaning, and surrounding context.
How it connects
Connects sarcasm with sentiment analysis, text classification, pretrained models, context, and idiomatic expressions.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Starting Data Analytics with Generative AI and Python. The section links point to the parts of the book it draws on.