Transforming Customer Digital Footprints into Decision Enablers in Hospitality
The proliferation of online hotel review platforms has prompted decision-makers in the hospitality sector to acknowledge the significance of extracting valuable information from this vast source. While contemporary research has primarily focused on extracting sentiment and discussion topics from onl...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/7/3114 |
| _version_ | 1797212844905725952 |
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| author | Achini Adikari Su Nguyen Rashmika Nawaratne Daswin De Silva Damminda Alahakoon |
| author_facet | Achini Adikari Su Nguyen Rashmika Nawaratne Daswin De Silva Damminda Alahakoon |
| author_sort | Achini Adikari |
| collection | DOAJ |
| description | The proliferation of online hotel review platforms has prompted decision-makers in the hospitality sector to acknowledge the significance of extracting valuable information from this vast source. While contemporary research has primarily focused on extracting sentiment and discussion topics from online reviews, the transformative potential of such insights remains largely untapped. In this paper, we propose an approach that leverages Natural Language Processing (NLP) techniques to convert unstructured textual reviews into a quantifiable and structured representation of emotions and hotel aspects. Building upon this derived representation, we conducted a segmentation analysis to gauge distinct emotion and concern-based profiles of customers, as well as profiles of hotels with similar customer emotions using a self-organizing unsupervised algorithm. We demonstrated the practicality of our approach using 22,450 online reviews collected from 44 hotels. The insights garnered from emotion analysis and review segmentation facilitate the development of targeted customer management strategies and informed decision-making. |
| first_indexed | 2024-04-24T10:48:51Z |
| format | Article |
| id | doaj.art-580ae2c2302140b6b62f81344dcff9e1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T10:48:51Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-580ae2c2302140b6b62f81344dcff9e12024-04-12T13:15:44ZengMDPI AGApplied Sciences2076-34172024-04-01147311410.3390/app14073114Transforming Customer Digital Footprints into Decision Enablers in HospitalityAchini Adikari0Su Nguyen1Rashmika Nawaratne2Daswin De Silva3Damminda Alahakoon4Research Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, AustraliaDepartment of Accounting, Information Systems & Supply Chain, RMIT University, Melbourne, VIC 3000, AustraliaResearch Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, AustraliaResearch Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, AustraliaResearch Centre for Data Analytics and Cognition, La Trobe University, Bundoora, VIC 3086, AustraliaThe proliferation of online hotel review platforms has prompted decision-makers in the hospitality sector to acknowledge the significance of extracting valuable information from this vast source. While contemporary research has primarily focused on extracting sentiment and discussion topics from online reviews, the transformative potential of such insights remains largely untapped. In this paper, we propose an approach that leverages Natural Language Processing (NLP) techniques to convert unstructured textual reviews into a quantifiable and structured representation of emotions and hotel aspects. Building upon this derived representation, we conducted a segmentation analysis to gauge distinct emotion and concern-based profiles of customers, as well as profiles of hotels with similar customer emotions using a self-organizing unsupervised algorithm. We demonstrated the practicality of our approach using 22,450 online reviews collected from 44 hotels. The insights garnered from emotion analysis and review segmentation facilitate the development of targeted customer management strategies and informed decision-making.https://www.mdpi.com/2076-3417/14/7/3114social media analyticsGuidedLDAemotion modellingunsupervised learningdecision supportonline reviews |
| spellingShingle | Achini Adikari Su Nguyen Rashmika Nawaratne Daswin De Silva Damminda Alahakoon Transforming Customer Digital Footprints into Decision Enablers in Hospitality Applied Sciences social media analytics GuidedLDA emotion modelling unsupervised learning decision support online reviews |
| title | Transforming Customer Digital Footprints into Decision Enablers in Hospitality |
| title_full | Transforming Customer Digital Footprints into Decision Enablers in Hospitality |
| title_fullStr | Transforming Customer Digital Footprints into Decision Enablers in Hospitality |
| title_full_unstemmed | Transforming Customer Digital Footprints into Decision Enablers in Hospitality |
| title_short | Transforming Customer Digital Footprints into Decision Enablers in Hospitality |
| title_sort | transforming customer digital footprints into decision enablers in hospitality |
| topic | social media analytics GuidedLDA emotion modelling unsupervised learning decision support online reviews |
| url | https://www.mdpi.com/2076-3417/14/7/3114 |
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