Understanding Online Reviews in All-Inclusive Hotels Servicescape: A Fuzzy Set Approach

Rassal, Carimo; Correia, Antónia; Serra, Francisco

doi:10.1080/1528008x.2023.2167761

Cited by 1 publication

(1 citation statement)

References 115 publications

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“…The research gap in this topic pertains to the need for further investigation into the long-term socio-economic and environmental implications of robot hotels and the digitalization of the tourism industry. While existing studies provide valuable insights into public sentiments and the adoption of digital technologies in accommodation services, there remains a dearth of research examining the broader systemic effects of these trends on local economies, cultural dynamics, and natural ecosystems [34]- [38]. Additionally, the limited exploration of alternative methodological approaches and the perspectives of diverse stakeholders highlight opportunities for future research to expand the scope and depth of inquiry in this field.…”

Section: Research Gap and Trends Mapping: Climate Change And Tourismmentioning

confidence: 99%

Sentiment Classification of Robot Hotel Content using NBC and SVM Algorithm

Singgalen

2024

JoSYC

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Sentiment analysis plays a pivotal role in comprehending public sentiment, notably within digital communication, where copious amounts of textual data are generated daily. This study delves into the efficacy of sentiment classification models, namely the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM), within the imbalanced datasets commonly encountered in sentiment analysis tasks. Employing a comparative analysis methodology, a dataset comprising robot hotel reviews from online platforms is the basis for evaluation. Both NBC and SVM models undergo training and assessment, with and without the Synthetic Minority Over-sampling Technique (SMOTE), to rectify the class imbalance. Performance evaluation relies on critical metrics, including accuracy, recall, precision, f-measure, and Area Under Curve (AUC) to gauge model effectiveness. Findings demonstrate SVM's superiority over NBC in terms of accuracy (SVM: 76.88%, NBC: 67.43%), precision (SVM: 92.03%, NBC: 86.87%), recall (SVM: 58.88%, NBC: 41.00%), f-measure (SVM: 71.78%, NBC: 55.63%), and AUC (SVM: 0.907, NBC: 0.961). Incorporating SMOTE significantly enhances both models' performance, particularly in addressing class imbalance concerns. Although NBC exhibits a more balanced performance across precision and recall metrics, SVM demonstrates heightened accuracy and predictive capability in sentiment classification tasks. These findings underscore the pivotal role of algorithm selection and preprocessing techniques in optimizing sentiment analysis performance, thereby providing invaluable insights for practitioners and researchers alike.

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Section: Research Gap and Trends Mapping: Climate Change And Tourismmentioning

confidence: 99%

Sentiment Classification of Robot Hotel Content using NBC and SVM Algorithm

Singgalen

2024

JoSYC

View full text Add to dashboard Cite

show abstract

Understanding Online Reviews in All-Inclusive Hotels Servicescape: A Fuzzy Set Approach

Cited by 1 publication

References 115 publications

Sentiment Classification of Robot Hotel Content using NBC and SVM Algorithm

Sentiment Classification of Robot Hotel Content using NBC and SVM Algorithm

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