Research Article

Transforming Customer Experience in the Airline Industry: A Comprehensive Analysis of Twitter Sentiments Using Machine Learning and Association Rule Mining

Authors

  • Maliha Tayaba Department of Computer Science, University of South Dakota, Vermillion, South Dakota, USA
  • Eftekhar Hossain Ayon Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA
  • Md Tuhin Mia School of Business, International American University, Los Angeles, California, USA
  • Malay Sarkar Department of Management Science and Quantitative Methods, Gannon University, USA
  • Rejon Kumar Ray Department of Business Analytics Business Analytics, Gannon University, USA
  • Md Salim Chowdhury College of Graduate and Professional Studies Trine University, USA
  • Md Al-Imran College of Graduate and Professional Studies Trine University, USA
  • Nur Nobe Department of Healthcare Management, Saint Francis College, Brooklyn, New York, USA
  • Bishnu Padh Ghosh Department of Healthcare Management, Saint Francis College, Brooklyn, New York, USA
  • MD Tanvir Islam Department of Computer Science, Monroe College, New Rochelle, New York, USA
  • Aisharyja Roy Puja Department of Management Science and Quantitative Methods, Gannon University, USA

Abstract

The airline industry places significant emphasis on improving customer experience, and Twitter has emerged as a key platform for passengers to share their opinions. This research introduces a machine learning approach to analyze tweets and enhance customer experience. Features are extracted from tweets using both the Glove dictionary and n-gram methods for word embedding. The study explores various artificial neural network (ANN) architectures and Support Vector Machines (SVM) to create a classification model for categorizing tweets into positive and negative sentiments. Additionally, a Convolutional Neural Network (CNN) is developed for tweet classification, and its performance is compared with the most accurate model identified among SVM and multiple ANN architectures. The results indicate that the CNN model surpasses the SVM and ANN models. To provide further insights, association rule mining is applied to different tweet categories, revealing connections with sentiment categories. These findings offer valuable information to help airline industries refine and enhance their customer experience strategies.

Article information

Journal

Journal of Computer Science and Technology Studies

Volume (Issue)

5 (4)

Pages

194-202

Published

2023-12-22

How to Cite

Tayaba, M., Ayon, E. H., Mia, M. T., Sarkar, M., Ray, R. K., Chowdhury, M. S., Al-Imran, M., Nobe, N., Ghosh, B. P., Islam, M. T., & Puja, A. R. (2023). Transforming Customer Experience in the Airline Industry: A Comprehensive Analysis of Twitter Sentiments Using Machine Learning and Association Rule Mining. Journal of Computer Science and Technology Studies, 5(4), 194–202. https://doi.org/10.32996/jcsts.2023.5.4.20

Downloads