Survival strategies for family-run homestays: analyzing user reviews through text mining

Online booking of homestays through e-travel portals is based on the virtual brand and perception, which are largely affected by user-generated electronic word-of-mouth (eWOM). With the objective of mining actionable insights from eWOM, this study conducted opinion mining for homestays located in fo...

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Published inData science and management Vol. 7; no. 3; pp. 228 - 237
Main Authors Krishnan, Jay, Bhattacharjee, Biplab, Pratap, Maheshwar, Yadav, Janardan Krishna, Maiti, Moinak
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.09.2024
KeAi Communications Co. Ltd
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Online AccessGet full text
ISSN2666-7649
2666-7649
DOI10.1016/j.dsm.2024.03.003

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Abstract Online booking of homestays through e-travel portals is based on the virtual brand and perception, which are largely affected by user-generated electronic word-of-mouth (eWOM). With the objective of mining actionable insights from eWOM, this study conducted opinion mining for homestays located in four thematic areas of Kerala. Accordingly, various techniques have been deployed, such as sentiment and emotional analyses, topic modeling, and clustering methods. Key themes revealed from topic modeling were breakfast, facilities provided, ambience, bathroom, cleanliness, hospitality exhibited, and satisfaction with the host. A lasso logistic regression-based predictive binary text classification model (with 97.6% accuracy) for homestay recommendations was developed. Our findings and predictive model have implications for managers and homestay owners in devising appropriate marketing strategies and improving their overall guest experience.
AbstractList Online booking of homestays through e-travel portals is based on the virtual brand and perception, which are largely affected by user-generated electronic word-of-mouth (eWOM). With the objective of mining actionable insights from eWOM, this study conducted opinion mining for homestays located in four thematic areas of Kerala. Accordingly, various techniques have been deployed, such as sentiment and emotional analyses, topic modeling, and clustering methods. Key themes revealed from topic modeling were breakfast, facilities provided, ambience, bathroom, cleanliness, hospitality exhibited, and satisfaction with the host. A lasso logistic regression-based predictive binary text classification model (with 97.6% accuracy) for homestay recommendations was developed. Our findings and predictive model have implications for managers and homestay owners in devising appropriate marketing strategies and improving their overall guest experience.
Author Maiti, Moinak
Bhattacharjee, Biplab
Pratap, Maheshwar
Yadav, Janardan Krishna
Krishnan, Jay
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Snippet Online booking of homestays through e-travel portals is based on the virtual brand and perception, which are largely affected by user-generated electronic...
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SubjectTerms Homestays
Predictive modeling
Text mining
Topic modeling
Tourism
Title Survival strategies for family-run homestays: analyzing user reviews through text mining
URI https://dx.doi.org/10.1016/j.dsm.2024.03.003
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