Case context: A team wants to build a system to examine online product reviews and automatically tell whether the writer liked or disliked the product, similar to the goal described in the source.

Question: A new review reads: 'I regret buying this--it broke after one use.' Using the concept of sentiment classification, what sentiment should this review be classified as, and what in the text supports that classification?

Sample answer: This review should be classified as highly negative. It uses the phrase 'I regret buying this,' which closely mirrors the source's negative example ('I regret buying it'), and it also describes a product failure ('it broke after one use'), both of which indicate dissatisfaction with the product.

Key points:

• The review should be classified as negative
• The phrase 'I regret buying this' signals dissatisfaction
• Describing the product breaking supports a negative sentiment
• Sentiment classification relies on identifying opinion-bearing language

Rubric: Full credit: correctly classifies the review as negative and identifies specific textual evidence (regret, product failure) supporting the classification. Partial credit: correct classification without clear evidence. No credit: incorrect classification.