Sensory Descriptor Analysis of Whisky Lexicons through the Use of Deep Learning
This paper is concerned with extracting relevant terms from a text corpus on whisk(e)y. “Relevant” terms are usually contextually defined in their domain of use. Arguably, every domain has a specialized vocabulary used for describing things. For example, the field of Sensory Science, a sub-field of...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Foods |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-8158/10/7/1633 |
| _version_ | 1797527153167826944 |
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| author | Chreston Miller Leah Hamilton Jacob Lahne |
| author_facet | Chreston Miller Leah Hamilton Jacob Lahne |
| author_sort | Chreston Miller |
| collection | DOAJ |
| description | This paper is concerned with extracting relevant terms from a text corpus on whisk(e)y. “Relevant” terms are usually contextually defined in their domain of use. Arguably, every domain has a specialized vocabulary used for describing things. For example, the field of Sensory Science, a sub-field of Food Science, investigates human responses to food products and differentiates “descriptive” terms for flavors from “ordinary”, non-descriptive language. Within the field, descriptors are generated through Descriptive Analysis, a method wherein a human panel of experts tastes multiple food products and defines descriptors. This process is both time-consuming and expensive. However, one could leverage existing data to identify and build a flavor language automatically. For example, there are thousands of professional and semi-professional reviews of whisk(e)y published on the internet, providing abundant descriptors interspersed with non-descriptive language. The aim, then, is to be able to automatically identify descriptive terms in unstructured reviews for later use in product flavor characterization. We created two systems to perform this task. The first is an interactive visual tool that can be used to tag examples of descriptive terms from thousands of whisky reviews. This creates a training dataset that we use to perform transfer learning using GloVe word embeddings and a Long Short-Term Memory deep learning model architecture. The result is a model that can accurately identify descriptors within a corpus of whisky review texts with a train/test accuracy of 99% and precision, recall, and F1-scores of 0.99. We tested for overfitting by comparing the training and validation loss for divergence. Our results show that the language structure for descriptive terms can be programmatically learned. |
| first_indexed | 2024-03-10T09:39:02Z |
| format | Article |
| id | doaj.art-327c350964304d28a2174ed9436c66da |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-10T09:39:02Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-327c350964304d28a2174ed9436c66da2023-11-22T03:48:17ZengMDPI AGFoods2304-81582021-07-01107163310.3390/foods10071633Sensory Descriptor Analysis of Whisky Lexicons through the Use of Deep LearningChreston Miller0Leah Hamilton1Jacob Lahne2Data Services, University Libraries, Virginia Tech, 560 Drillfield Dr., Blacksburg, VA 24061, USADepartment of Food Science & Technology, Virginia Tech, Blacksburg, VA 24061, USADepartment of Food Science & Technology, Virginia Tech, Blacksburg, VA 24061, USAThis paper is concerned with extracting relevant terms from a text corpus on whisk(e)y. “Relevant” terms are usually contextually defined in their domain of use. Arguably, every domain has a specialized vocabulary used for describing things. For example, the field of Sensory Science, a sub-field of Food Science, investigates human responses to food products and differentiates “descriptive” terms for flavors from “ordinary”, non-descriptive language. Within the field, descriptors are generated through Descriptive Analysis, a method wherein a human panel of experts tastes multiple food products and defines descriptors. This process is both time-consuming and expensive. However, one could leverage existing data to identify and build a flavor language automatically. For example, there are thousands of professional and semi-professional reviews of whisk(e)y published on the internet, providing abundant descriptors interspersed with non-descriptive language. The aim, then, is to be able to automatically identify descriptive terms in unstructured reviews for later use in product flavor characterization. We created two systems to perform this task. The first is an interactive visual tool that can be used to tag examples of descriptive terms from thousands of whisky reviews. This creates a training dataset that we use to perform transfer learning using GloVe word embeddings and a Long Short-Term Memory deep learning model architecture. The result is a model that can accurately identify descriptors within a corpus of whisky review texts with a train/test accuracy of 99% and precision, recall, and F1-scores of 0.99. We tested for overfitting by comparing the training and validation loss for divergence. Our results show that the language structure for descriptive terms can be programmatically learned.https://www.mdpi.com/2304-8158/10/7/1633natural language processingdeep learningsensory scienceflavor lexiconlong short-term memory |
| spellingShingle | Chreston Miller Leah Hamilton Jacob Lahne Sensory Descriptor Analysis of Whisky Lexicons through the Use of Deep Learning Foods natural language processing deep learning sensory science flavor lexicon long short-term memory |
| title | Sensory Descriptor Analysis of Whisky Lexicons through the Use of Deep Learning |
| title_full | Sensory Descriptor Analysis of Whisky Lexicons through the Use of Deep Learning |
| title_fullStr | Sensory Descriptor Analysis of Whisky Lexicons through the Use of Deep Learning |
| title_full_unstemmed | Sensory Descriptor Analysis of Whisky Lexicons through the Use of Deep Learning |
| title_short | Sensory Descriptor Analysis of Whisky Lexicons through the Use of Deep Learning |
| title_sort | sensory descriptor analysis of whisky lexicons through the use of deep learning |
| topic | natural language processing deep learning sensory science flavor lexicon long short-term memory |
| url | https://www.mdpi.com/2304-8158/10/7/1633 |
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