From Words to Ratings: Machine Learning and NLP for Wine Reviews
Wine production is an important sector of the food industry in Bulgaria, contributing to both economic development and cultural heritage. The present study aims to show how natural language processing (NLP) and machine learning methods can be applied to analyze expert-written Bulgarian wine descript...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Beverages |
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| Online Access: | https://www.mdpi.com/2306-5710/11/3/80 |
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| author | Iliana Ilieva Margarita Terziyska Teofana Dimitrova |
| author_facet | Iliana Ilieva Margarita Terziyska Teofana Dimitrova |
| author_sort | Iliana Ilieva |
| collection | DOAJ |
| description | Wine production is an important sector of the food industry in Bulgaria, contributing to both economic development and cultural heritage. The present study aims to show how natural language processing (NLP) and machine learning methods can be applied to analyze expert-written Bulgarian wine descriptions and to extract patterns related to wine quality and style. Based on a bilingual dataset of reviews (in Bulgarian and English), semantic analysis, classification, regression and clustering models were used, which combine textual and structured data. The descriptions were transformed into numerical representations using a pre-trained language model (BERT), after which algorithms were used to predict style categories and ratings. Additional sentiment and segmentation analyses revealed differences between wine types, and clustering identified thematic structures in the expert language. The comparison between predefined styles and automatically derived clusters was evaluated using metrics such as Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). The resulting analysis shows that text descriptions contain valuable information that allows for automated wine profiling. These findings can be applied by a wide range of stakeholders—researchers, producers, retailers, and marketing specialists. |
| format | Article |
| id | doaj-art-c70cd8c7bf144b00a89561a10fdc6b97 |
| institution | Kabale University |
| issn | 2306-5710 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Beverages |
| spelling | doaj-art-c70cd8c7bf144b00a89561a10fdc6b972025-08-20T03:26:53ZengMDPI AGBeverages2306-57102025-06-011138010.3390/beverages11030080From Words to Ratings: Machine Learning and NLP for Wine ReviewsIliana Ilieva0Margarita Terziyska1Teofana Dimitrova2Faculty of Economics, University of Food Technologies, 4000 Plovdiv, BulgariaFaculty of Economics, University of Food Technologies, 4000 Plovdiv, BulgariaFaculty of Economic and Social Sciences, University of Plovdiv Paisii Hilendarski, 4000 Plovdiv, BulgariaWine production is an important sector of the food industry in Bulgaria, contributing to both economic development and cultural heritage. The present study aims to show how natural language processing (NLP) and machine learning methods can be applied to analyze expert-written Bulgarian wine descriptions and to extract patterns related to wine quality and style. Based on a bilingual dataset of reviews (in Bulgarian and English), semantic analysis, classification, regression and clustering models were used, which combine textual and structured data. The descriptions were transformed into numerical representations using a pre-trained language model (BERT), after which algorithms were used to predict style categories and ratings. Additional sentiment and segmentation analyses revealed differences between wine types, and clustering identified thematic structures in the expert language. The comparison between predefined styles and automatically derived clusters was evaluated using metrics such as Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). The resulting analysis shows that text descriptions contain valuable information that allows for automated wine profiling. These findings can be applied by a wide range of stakeholders—researchers, producers, retailers, and marketing specialists.https://www.mdpi.com/2306-5710/11/3/80wine reviewsnatural language processingmachine learningtext-based predictionwine style classification |
| spellingShingle | Iliana Ilieva Margarita Terziyska Teofana Dimitrova From Words to Ratings: Machine Learning and NLP for Wine Reviews Beverages wine reviews natural language processing machine learning text-based prediction wine style classification |
| title | From Words to Ratings: Machine Learning and NLP for Wine Reviews |
| title_full | From Words to Ratings: Machine Learning and NLP for Wine Reviews |
| title_fullStr | From Words to Ratings: Machine Learning and NLP for Wine Reviews |
| title_full_unstemmed | From Words to Ratings: Machine Learning and NLP for Wine Reviews |
| title_short | From Words to Ratings: Machine Learning and NLP for Wine Reviews |
| title_sort | from words to ratings machine learning and nlp for wine reviews |
| topic | wine reviews natural language processing machine learning text-based prediction wine style classification |
| url | https://www.mdpi.com/2306-5710/11/3/80 |
| work_keys_str_mv | AT ilianailieva fromwordstoratingsmachinelearningandnlpforwinereviews AT margaritaterziyska fromwordstoratingsmachinelearningandnlpforwinereviews AT teofanadimitrova fromwordstoratingsmachinelearningandnlpforwinereviews |