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Mining online e-liquid reviews for opinion polarities about e-liquid features
BACKGROUND: In recent years, the emerging electronic cigarette (e-cigarette) marketplace has developed prosperously all over the world. By analyzing online e-liquid reviews, we seek to identify the features attracting users. METHODS: We collected e-liquid reviews from one of the largest online e-liq...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5501340/ https://www.ncbi.nlm.nih.gov/pubmed/28683797 http://dx.doi.org/10.1186/s12889-017-4533-z |
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author | Chen, Zhipeng Zeng, Daniel D. |
author_facet | Chen, Zhipeng Zeng, Daniel D. |
author_sort | Chen, Zhipeng |
collection | PubMed |
description | BACKGROUND: In recent years, the emerging electronic cigarette (e-cigarette) marketplace has developed prosperously all over the world. By analyzing online e-liquid reviews, we seek to identify the features attracting users. METHODS: We collected e-liquid reviews from one of the largest online e-liquid review websites and extracted the e-liquid features by keywords. Then we used sentiment analysis to classify the features into two polarities: positive and negative. The positive sentiment ratio of a feature reflects the e-cigarette users’ preference on this feature. RESULTS: The popularity and preference of e-liquid features are not correlated. Nuts and cream are the favorite flavor categories, while fruit and cream are the most popular categories. The top mixed flavors are preferable to single flavors. Fruit and cream categories are most frequently mixed with other flavors. E-cigarette users are satisfied with cloud production, but not satisfied with the ingredients and throat hit. CONCLUSIONS: We identified the flavors that e-cigarette users were satisfied with, and we found the users liked e-cigarette cloud production. Therefore, flavors and cloud production are potential factors attracting new users. |
format | Online Article Text |
id | pubmed-5501340 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55013402017-07-10 Mining online e-liquid reviews for opinion polarities about e-liquid features Chen, Zhipeng Zeng, Daniel D. BMC Public Health Research Article BACKGROUND: In recent years, the emerging electronic cigarette (e-cigarette) marketplace has developed prosperously all over the world. By analyzing online e-liquid reviews, we seek to identify the features attracting users. METHODS: We collected e-liquid reviews from one of the largest online e-liquid review websites and extracted the e-liquid features by keywords. Then we used sentiment analysis to classify the features into two polarities: positive and negative. The positive sentiment ratio of a feature reflects the e-cigarette users’ preference on this feature. RESULTS: The popularity and preference of e-liquid features are not correlated. Nuts and cream are the favorite flavor categories, while fruit and cream are the most popular categories. The top mixed flavors are preferable to single flavors. Fruit and cream categories are most frequently mixed with other flavors. E-cigarette users are satisfied with cloud production, but not satisfied with the ingredients and throat hit. CONCLUSIONS: We identified the flavors that e-cigarette users were satisfied with, and we found the users liked e-cigarette cloud production. Therefore, flavors and cloud production are potential factors attracting new users. BioMed Central 2017-07-07 /pmc/articles/PMC5501340/ /pubmed/28683797 http://dx.doi.org/10.1186/s12889-017-4533-z Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Chen, Zhipeng Zeng, Daniel D. Mining online e-liquid reviews for opinion polarities about e-liquid features |
title | Mining online e-liquid reviews for opinion polarities about e-liquid features |
title_full | Mining online e-liquid reviews for opinion polarities about e-liquid features |
title_fullStr | Mining online e-liquid reviews for opinion polarities about e-liquid features |
title_full_unstemmed | Mining online e-liquid reviews for opinion polarities about e-liquid features |
title_short | Mining online e-liquid reviews for opinion polarities about e-liquid features |
title_sort | mining online e-liquid reviews for opinion polarities about e-liquid features |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5501340/ https://www.ncbi.nlm.nih.gov/pubmed/28683797 http://dx.doi.org/10.1186/s12889-017-4533-z |
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