Case context: Your team is building a classifier for an online marketplace that labels short product comments as favorable or unfavorable. A colleague proposes a multi-stage system: first run a dependency parser to extract nouns and opinion words, then send those extracted features to a separate classifier.

Question: What model style would you recommend instead, and how would it handle the comment "Warm jacket, but the zipper failed after one week." compared with the proposed multi-stage system?

Sample answer: Recommend an end-to-end sentiment classifier. This design learns one mapping from the raw comment text to the label, rather than relying on a hand-built sequence of parsing and feature selection steps. For the example comment, the model would read the full sentence as input and learn to assign the positive or negative label directly from the text.

Key points:

• Recommend an end-to-end classifier.
• Explain that it learns from the full text without separate linguistic pre-processing.
• State that the sample comment is fed in unchanged as raw input.
• Note that it predicts sentiment directly, without an extraction-and-classification chain.

Rubric: The response must name the end-to-end approach and explain that it replaces the multi-stage parser-based pipeline by learning directly from the original text.