Listen to the guests: Text-mining Airbnb reviews to explore indoor environmental quality

Villeneuve, Hannah; O’Brien, William

doi:10.1016/j.buildenv.2019.106555

Cited by 47 publications

(35 citation statements)

References 25 publications

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“…Several studies find that consumer satisfaction is also significantly influenced by Airbnb service ( Ju et al., 2019 ; Tussyadiah and Zach, 2017 ), facilities (e.g., indoor environmental quality) ( Villeneuve and O'Brien, 2020 ), location, feeling welcome ( Tussyadiah and Zach, 2017 ), user characteristics ( Moro et al., 2019 ), perceived value ( Jiang et al., 2019 ), and face-to-face interaction between guests and host ( Moon et al., 2019a ).…”

Section: Discussionmentioning

confidence: 99%

A decade of systematic literature review on Airbnb: the sharing economy from a multiple stakeholder perspective

Hati¹,

Balqiah²,

Hananto³

et al. 2021

Heliyon

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Airbnb, which launched its business in 2009, has experienced explosive growth by creating value through the sharing economy business model. The Airbnb business model helps property owners exploit underutilized assets. However, along with its rapid growth, controversies have arisen among many stakeholders, especially the traditional hotel industry, communities, and policymakers. This study reviews academic articles to pinpoint the factors involved in the relationships among Airbnb and its multiple stakeholders. The aim is to identify the benefits, drawbacks, and issues surrounding Airbnb. The analysis is based on the perspectives of six Airbnb stakeholders: guests, hosts, employees, communities, competitors, and policymakers. A variety of scholarly journals indexed in the Scopus database were reviewed, with 282 included in the final analysis. The analysis will be useful for academics, practitioners, and policymakers alike, as it summarizes the Airbnb relevant actors, identifies key factors that influence stakeholder behavior, and assesses the power and level of influence of each stakeholder. Ultimately, the study points to potential directions for future research on Airbnb.

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Section: Discussionmentioning

confidence: 99%

A decade of systematic literature review on Airbnb: the sharing economy from a multiple stakeholder perspective

Hati¹,

Balqiah²,

Hananto³

et al. 2021

Heliyon

View full text Add to dashboard Cite

show abstract

“…One way to extract text data more naturally is by using the Text Mining technique. Text Mining has invaluable commercial value since it's a simplified and faster way to collect data and can be applied in different areas (57)(58)(59)(60)(61)(62)(63)(64)(65)(66)(67)(68)(69)(70)(71)(72)(73).…”

Section: Discussionmentioning

confidence: 99%

Drug delivery polymers: An Analysis Based on Literature Text Mining

Daher¹,

Souza²,

Carelo³

et al. 2021

BJEDIS

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Many polymers are used as drug carriers for controlled delivery because of its many advantages. To find the most used polymers for this purpose in recent years, we chose to use Text Mining as an evaluation tool. Text Mining tool was precious in this work to identify the main polymers that contributed to drug delivery, allowing us to draw a map year by year based on correlation analyzes. Firstly, an analysis was made on what was most researched per year, and then what were the most cited polymers. Then the most mentioned polymers were separated and organized to observe which year it was more or less cited. It was found with this work that none of the polymers showed only positive correlations in all years. Besides, polymers did not reveal individual growth in their use over the years. The Text Mining presented here proved to be an efficient and quick way to observe the desired theme and open up possibilities for research in different fields.

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“…Park and Kremer studied the classification of ecological sustainability indicators based on a TM approach [47]. Rabiei used TM to elucidate knowledge gaps and priorities in Iranian environmental science [48], while Villeneuve et al explored indoor environmental quality based on Airbnb customer reviews using TM [49]. Wang et al assessed the environmental performance in tourist areas and used TM of online news to explore the influencing factors [50].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Environmental Information Disclosure of Listed Companies in China’s Heavy Pollution Industries: A Text Mining-Based Methodology

Cai

Deng

2021

Sustainability

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Environmental information disclosure (EID) of listed companies is a significant and essential reference for assessing their environmental protection commitment. However, the content and form of EID are complex, and previous assessment studies involved manual scoring mainly by the experts in this field. It is subjective and has low timeliness. Therefore, this paper proposes an automatic evaluation framework of EID quality based on text mining (TM), including the EID index system’s construction, automatic scoring of environmental information disclosure quality, and EID index calculation. Furthermore, based on the EID of 801 listed companies in China’s heavy pollution industry from 2013 to 2017, case studies are conducted. The case study results show that the overall quality of the EID of listed companies in China’s heavily polluting industries is low, and there is a gap differentiation between the 16 industries. Compared with the subjective manual scoring method, TM evaluation can evaluate the quality of EID more effectively and accurately. It has great potential and can become an essential tool for the sustainable development of society and listed companies.

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Listen to the guests: Text-mining Airbnb reviews to explore indoor environmental quality

Cited by 47 publications

References 25 publications

A decade of systematic literature review on Airbnb: the sharing economy from a multiple stakeholder perspective

A decade of systematic literature review on Airbnb: the sharing economy from a multiple stakeholder perspective

Drug delivery polymers: An Analysis Based on Literature Text Mining

Evaluation of Environmental Information Disclosure of Listed Companies in China’s Heavy Pollution Industries: A Text Mining-Based Methodology

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