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利用产品评论数据集进行新的系统气味词汇收集和分类。

A new systematic collection and classification of odour words by using a product review dataset.

机构信息

Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan.

Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Ibaraki, Japan.

出版信息

PLoS One. 2023 Aug 10;18(8):e0289368. doi: 10.1371/journal.pone.0289368. eCollection 2023.

DOI:10.1371/journal.pone.0289368
PMID:37561717
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10414683/
Abstract

The odours encountered on a daily basis are dependent on an individual's society and culture. Therefore, when conducting olfactory tests, the odour stimuli utilized must be appropriate for the individual's environment. In this study, we gathered and classified the odours experienced by Japanese individuals in their daily lives through a large dataset of product reviews encompassing food and household items. Specifically, we performed morphological analysis on product review sentences in Japanese that contained odour descriptions, and we compiled the nouns used to describe odours. A total of 617,208 sentences that reviewed odour experiences and their corresponding nouns were collected. The top 100 frequently used odour nouns were classified into 15 clusters according to the context in which they were used. The methodology employed in collecting and classifying odour nouns as presented in this study can be utilized in other situations. It can assist in selecting appropriate odour stimuli for the olfactory test based on the society, culture, and time period.

摘要

日常生活中所能闻到的气味取决于个人所属的社会和文化。因此,在进行嗅觉测试时,所使用的气味刺激物必须适合个人的环境。在这项研究中,我们通过包含食品和家居用品的大量产品评论数据集,收集并分类了日本人在日常生活中所经历的气味。具体来说,我们对包含气味描述的日语产品评论句子进行了形态分析,并编写了用于描述气味的名词。共收集了 617208 条评论气味体验及其相应名词的句子。使用出现语境对前 100 个常用气味名词进行分类,共分为 15 个聚类。本研究中收集和分类气味名词的方法可以应用于其他情况。它可以根据社会、文化和时间段,帮助选择适合嗅觉测试的气味刺激物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c82/10414683/002d523e2cf2/pone.0289368.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c82/10414683/a87653a22066/pone.0289368.g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c82/10414683/da284ef9472f/pone.0289368.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c82/10414683/002d523e2cf2/pone.0289368.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c82/10414683/a87653a22066/pone.0289368.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c82/10414683/dfbe86a7026b/pone.0289368.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c82/10414683/af7e0241d307/pone.0289368.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c82/10414683/da284ef9472f/pone.0289368.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c82/10414683/002d523e2cf2/pone.0289368.g005.jpg

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引用本文的文献

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Correction: A new systematic collection and classification of odour words by using a product review dataset.更正:通过使用产品评论数据集对气味词汇进行新的系统收集和分类。
PLoS One. 2024 Nov 21;19(11):e0314583. doi: 10.1371/journal.pone.0314583. eCollection 2024.

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