Structured Attribute Extraction from Product Reviews

This paper studies automatic attribute extraction and sentiment assignment from product reviews. The task is to recover product aspects from free-form customer reviews and label them as positive, negative, or irrelevant. The modeling progression runs from two lightweight heuristics to two discriminative sequence models: a noun-driven bag-of-words baseline, a nearest-adjective sentiment heuristic, a Maximum Entropy Markov Model (MEMM), and a Conditional Random Field (CRF). The empirical pattern is clear. Heuristic models are adequate for high-recall noun harvesting and for recognizing the majority NONE class, but they struggle to assign polarity reliably because sentiment is often non-local and multiple aspects can appear in the same sentence. Sequence models improve substantially by conditioning on lexical, orthographic, part-of-speech, chunk, and contextual cues. Feature-level F1 rises into the high 90s for MEMM and CRF, while polarity-specific F1 remains meaningfully harder, especially under cross-domain transfer. The strongest overall conclusions are that language structure matters more than sentence-level sentiment alone, and that future gains will come from better modeling of multi-word aspects, longer-range dependencies, and domain shift.