Decoding Wine Narratives with Hierarchical Attention: Classification, Visual Prompts, and Emerging E-Commerce Possibilities
में बचाया:
| में प्रकाशित: | Journal of Theoretical and Applied Electronic Commerce Research vol. 20, no. 3 (2025), p. 212-251 |
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| मुख्य लेखक: | |
| अन्य लेखक: | , , |
| प्रकाशित: |
MDPI AG
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| विषय: | |
| ऑनलाइन पहुंच: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| टैग: |
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MARC
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| 001 | 3254567663 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0718-1876 | ||
| 024 | 7 | |a 10.3390/jtaer20030212 |2 doi | |
| 035 | |a 3254567663 | ||
| 045 | 2 | |b d20250701 |b d20250930 | |
| 084 | |a 78730 |2 nlm | ||
| 100 | 1 | |a Diaconita Vlad | |
| 245 | 1 | |a Decoding Wine Narratives with Hierarchical Attention: Classification, Visual Prompts, and Emerging E-Commerce Possibilities | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Wine reviews can connect words to flavours; they entwine sensory experiences into vivid stories. This research explores the intersection of artificial intelligence and oenology by using state-of-the-art neural networks to decipher the nuances in wine reviews. For more accurate wine classification and to capture the essence of what matters most to aficionados, we use Hierarchical Attention Networks enhanced with pre-trained embeddings. We also propose an approach to create captivating marketing images using advanced text-to-image generation models, mining a large review corpus for the most important descriptive terms and thus linking textual tasting notes to automatically generated imagery. Compared to more conventional models, our results show that hierarchical attention processes fused with rich linguistic embeddings better reflect the complexities of wine language. In addition to improving the accuracy of wine classification, this method provides consumers with immersive experiences by turning sensory descriptors into striking visual stories. Ultimately, our research helps modernise wine marketing and consumer engagement by merging deep learning with sensory analytics, proving how technology-driven solutions can amplify storytelling and shopping experiences in the digital marketplace. | |
| 653 | |a Modernization | ||
| 653 | |a Machine learning | ||
| 653 | |a Text categorization | ||
| 653 | |a Marketing | ||
| 653 | |a Deep learning | ||
| 653 | |a Classification | ||
| 653 | |a Neural networks | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Sentiment analysis | ||
| 653 | |a Customer feedback | ||
| 653 | |a Product reviews | ||
| 653 | |a Social networks | ||
| 653 | |a Natural language processing | ||
| 653 | |a Electronic commerce | ||
| 653 | |a Image processing | ||
| 700 | 1 | |a Belciu Anda | |
| 700 | 1 | |a Corbea Alexandra Maria Ioana | |
| 700 | 1 | |a Simonca Iuliana | |
| 773 | 0 | |t Journal of Theoretical and Applied Electronic Commerce Research |g vol. 20, no. 3 (2025), p. 212-251 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3254567663/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3254567663/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3254567663/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |