The language of scents: a corpus-based study of Chinese perfume descriptions

Olfaction is one of the least explored senses in research, partly due to the difficulty of expressing olfactory experiences through language. While most studies on odor descriptions focus on English-language data, there is limited research on how odors are described in Chinese, particularly in large-scale, corpus-based studies. This study aims to explore the vocabulary and patterns used by Chinese speakers to describe scents. We analyzed 29,372 reviews of 36 different perfumes collected from the NoseTime website. Through grounded theory–informed qualitative analysis, topic modeling, and sentiment analysis, we examined how Chinese users describe scents, the types of vocabulary they use, and their emotional responses to perfumes. The results show that Chinese odor descriptions can be categorized into three types: entity-based descriptions, abstract descriptions, and evaluative descriptions, similar to those found in English. Perfume types are described in distinct ways based on their composition, and subjective perceptions of odors lead to variation in descriptions, even for the same scent. Within the entity-based category, source-based descriptions—referring to nouns that identify specific or generic odor sources—are the most prevalent. Overall, more than 70% of reviews were positive, and source-based terms dominated in both positive and negative reviews. These findings provide new insights into the linguistic and emotional aspects of olfactory experiences in Chinese-speaking populations.

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Publication Details

Journal
Humanities and Social Sciences Communications
Published
2026-05-09
DOI
https://doi.org/10.1057/s41599-026-07494-4
Primary Topic
Olfactory and Sensory Function Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

The language of scents: a corpus-based study of Chinese perfume descriptions

Han Zhou, Laiquan Zou, Zhuofei Zhu, Kexin Zhou
Humanities and Social Sciences Communications
Olfactory and Sensory Function Studies
article

The language of scents: a corpus-based study of Chinese perfume descriptions

Han Zhou, Laiquan Zou, Zhuofei Zhu, Kexin Zhou
article en

Abstract

Olfaction is one of the least explored senses in research, partly due to the difficulty of expressing olfactory experiences through language. While most studies on odor descriptions focus on English-language data, there is limited research on how odors are described in Chinese, particularly in large-scale, corpus-based studies. This study aims to explore the vocabulary and patterns used by Chinese speakers to describe scents. We analyzed 29,372 reviews of 36 different perfumes collected from the NoseTime website. Through grounded theory–informed qualitative analysis, topic modeling, and sentiment analysis, we examined how Chinese users describe scents, the types of vocabulary they use, and their emotional responses to perfumes. The results show that Chinese odor descriptions can be categorized into three types: entity-based descriptions, abstract descriptions, and evaluative descriptions, similar to those found in English. Perfume types are described in distinct ways based on their composition, and subjective perceptions of odors lead to variation in descriptions, even for the same scent. Within the entity-based category, source-based descriptions—referring to nouns that identify specific or generic odor sources—are the most prevalent. Overall, more than 70% of reviews were positive, and source-based terms dominated in both positive and negative reviews. These findings provide new insights into the linguistic and emotional aspects of olfactory experiences in Chinese-speaking populations.

Humanities and Social Sciences Communications
Zhujiang Hospital (CN), Southern Medical University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 5%
Olfactory and Sensory Function Studies
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The language of scents: a corpus-based study of Chinese perfume descriptions — Han Zhou, Laiquan Zou, et al. · Humanities and Social Sciences Communications (2026) | TGRS Research Map | TGRS