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Exploring Tech-Enabled Solutions: Evaluating Sentiment Classification Models for Airline Reviews
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Author(s): Sateesh Kumar T. K. (Kristu Jayanti College, India), Lijo P. Thomas (Kristu Jayanti College, India), Vishnu Achutha Menon (Central University of Tamil Nadu, India)and Juby Thomas (Kristu Jayanti College, India)
Copyright: 2024
Pages: 25
Source title:
Impact of AI and Tech-Driven Solutions in Hospitality and Tourism
Source Author(s)/Editor(s): Mohammad Badruddoza Talukder (International University of Business Agriculture and Technology, Bangladesh), Sanjeev Kumar (Lovely Professional University, India)and Pankaj Kumar Tyagi (University Institute of Tourism and Hospitality Management, Chandigarh University, Mohali, India)
DOI: 10.4018/979-8-3693-6755-1.ch014
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Abstract
This study explores passenger satisfaction and airline service quality within the travel and hospitality sector. The dataset offers valuable insights into customer sentiments and provides essential data for customer service enhancements and predictive modeling. Various data analysis techniques, including confusion matrix, multinomial regression, and specificity sensitivity analysis, are employed to thoroughly examine patterns, correlations, and predictive factors related to passenger satisfaction and airline service quality. The analysis reveals exceptional accuracy in sentiment classification, with perfect precision, recall, and F1-score across all sentiment categories. Multinomial regression analysis shows impressive accuracy, surpassing the baseline and remaining robust across various sentiment categories. Metrics like sensitivity, specificity, precision, and negative predictive value affirm the model's effectiveness in sentiment classification. Word cloud analysis reveals prominent themes in customer reviews, including “flight,” “service,” and other pertinent keywords.
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