Unveiling Dual Quality in Product Reviews: An NLP-Based Approach
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908378963378176 |
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| author | Poświata, Rafał Mirończuk, Marcin Michał Dadas, Sławomir Grębowiec, Małgorzata Perełkiewicz, Michał |
| author_facet | Poświata, Rafał Mirończuk, Marcin Michał Dadas, Sławomir Grębowiec, Małgorzata Perełkiewicz, Michał |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19254 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unveiling Dual Quality in Product Reviews: An NLP-Based Approach Poświata, Rafał Mirończuk, Marcin Michał Dadas, Sławomir Grębowiec, Małgorzata Perełkiewicz, Michał Computation and Language 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. |
| title | Unveiling Dual Quality in Product Reviews: An NLP-Based Approach |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.19254 |