Evaluate sentiment on mixed and sarcastic examples

A sentiment model can score clear praise and complaints well while failing exactly where interpretation matters. Mixed statements contain competing aspects. Sarcasm can reverse literal wording. Evaluate these as named slices rather than averaging them into an easy test.

A binary model meets four-label reality

The local NLP lab trains a binary TF-IDF classifier on repeated clear positive and negative phrases. It then sends six diagnostic examples: two mixed, two sarcastic and two clear.

from nlp_cases import sentiment_edge_case

result = sentiment_edge_case()
assert result["slice_counts"] == {"mixed": 2, "sarcastic": 2, "clear": 2}
for row in result["hard_rows"]:
    print(row["slice"], row["prediction"], row["text"])

The model predicts positive for both sarcastic examples: “Amazing, another reset link that fails” and “Perfect service, except nothing was fixed.” It also predicts positive for both mixed examples. The two clear controls receive the expected signs.

The mixed and sarcastic examples do not have binary gold labels because the training policy cannot express them. Calling those predictions “wrong” would hide the more important failure: the label space is inadequate.

Score the right task

Add mixed and uncertain classes or predict sentiment by aspect if that supports the action. Double-annotate difficult examples and preserve disagreements. Report per-slice counts and confusion rather than one overall percentage.

Construct hard cases before model selection. A handpicked failure gallery created after test inspection is valuable exploration but not an unbiased estimate. Validate any model change on a new held-out set.

Define safe behavior

If mixed or low-confidence text routes to human review, evaluate coverage and review volume. Do not let a positive keyword suppress an unresolved refund or safety complaint. Sentiment should not be the sole gate for high-impact escalation.

The Data Science course emphasizes label adequacy, slice evaluation and operational fallback over a headline accuracy number.

Exercise

Create 20 examples for each of clear, mixed, sarcastic and uncertain. Measure agreement, train a four-label baseline and evaluate an abstention policy. Report every slice with denominators.

Continue learning

This article is part of the NLP and text analytics sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Sentiment analysis: define the label before training.
• Continue with Named entity recognition with boundary-level evaluation.

Reference: scikit-learn classification metrics.