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Unveiling Dual Quality in Product Reviews: An NLP-Based Approach

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arxiv 2505.19254 v1 pith:3ZAHQ47U submitted 2025-05-25 cs.CL

Unveiling Dual Quality in Product Reviews: An NLP-Based Approach

Rafa{\l} Po\'swiata , Marcin Micha{\l} Miro\'nczuk , S{\l}awomir Dadas , Ma{\l}gorzata Gr\k{e}bowiec , Micha{\l} Pere{\l}kiewicz This is my paper
classification cs.CL
keywords qualitydualproductreviewsadditionallyaddressanalysisapplications
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Consumers often face inconsistent product quality, particularly when identical products vary between markets, a situation known as the dual quality problem. To identify and address this issue, automated techniques are needed. This paper explores how natural language processing (NLP) can aid in detecting such discrepancies and presents the full process of developing a solution. First, we describe in detail the creation of a new Polish-language dataset with 1,957 reviews, 540 highlighting dual quality issues. We then discuss experiments with various approaches like SetFit with sentence-transformers, transformer-based encoders, and LLMs, including error analysis and robustness verification. Additionally, we evaluate multilingual transfer using a subset of opinions in English, French, and German. The paper concludes with insights on deployment and practical applications.

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