Evaluating Airline Service Quality Through the Comprehensive Text-mining and TOPSIS-VIKOR-AISM Analysis

TL;DR

This paper presents a comprehensive framework combining text mining and multi-criteria decision-making methods to evaluate airline service quality based on online reviews, addressing previous research limitations in sample size and efficiency.

Contribution

It introduces a novel integrated approach using LSA, sentiment analysis, and TOPSIS-VIKOR-AISM for airline ranking, enhancing reliability and depth of service quality assessment.

Findings

Effective identification of key themes and sentiments from reviews

Improved airline ranking accuracy through combined methods

Insights into airline service quality evaluation

Abstract

Service quality rankings are pivotal for maintaining sustainability in the fiercely competitive airline industry. However, prior research in this domain has often fallen short in aspects of sample size, efficiency, and dependability. This study introduces refined insights into this area and establishes a comprehensive, yet highly elucidative, ranking framework. Initially, we employ Latent Semantic Analysis (LSA) to distill principal themes and sentiments from online reviews of 80 airlines. Subsequently, we utilize the SentiWordNet lexicon and the TextBlob package for conducting sentiment analysis based on these reviews. Following this, we construct a hierarchical structure using the computation of compromise solutions, employing an integrated Technique for Order Preference by Similarity to Ideal Solution, vis-\`a-vis Kriterijumska Optimizacija I Kompromisno Resenje-Adversarial Interpretive Structural Model (TOPSIS-VIKOR-AISM) methodology. Beyond aiding consumer decision-making and fostering airline growth, this study contributes novel viewpoints on evaluating the efficacy of airlines and other sectors.