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All issues Volume 448 (2023) E3S Web Conf., 448 (2023) 02044 References
Open Access
Issue
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
Article Number 02044
Number of page(s) 10
Section Information System
DOI https://doi.org/10.1051/e3sconf/202344802044
Published online 17 November 2023
  1. Siering, Michael. Amit V. Deokar, Christian Janze. “Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews.” Decis. Support Syst. 107: 52-63, (2018). [CrossRef] [Google Scholar]
  2. Marco, Julio Navio. Luis Manuel Ruiz-Gómez, Claudia Sevilla-Sevilla. Progress in information technology and tourism management: 30 years on and 20 years after the internet-Revisiting Buhalis & Law’s landmark study about eTourism. Tour. Manag. Vol 69, pp. 460–470, Dec (2018). [CrossRef] [Google Scholar]
  3. Ukpabi D, S Olaleye, E Mogaji, H Karjaluoto. Insights into online reviews of hotel service attributes: A cross-national study of selected countries in Africa. Inf. Technol. Tour. pp 243–256, (2018). [Google Scholar]
  4. Akshi Kumar, Geetanjali Garg. Systematic literature review on context-based sentiment analysis in social multimedia. Multimed. Tools Appl. 79, 15349–15380, (2019). [Google Scholar]
  5. Imene Guellil, Kamel Boukhalfa. Social big data mining: A survey focused on opinion mining and sentiments analysis. In Proceedings of the 2015 12th International Symposium on Programming and Systems (ISPS’15). IEEE, Los Alamitos, CA, 1-10, (2015). [Google Scholar]
  6. Chih-Fong Tsai, Kuanchin Chen, Ya-Han Hu, Wei-Kai Chen. Improving text summarization of online hotel reviews with review helpfulness and sentiment. Tour. Manag. 80, 104122, (2020). [Google Scholar]
  7. Jain, Praphula Kumar. Ephrem Admasu Yekun, Rajendra Pamula, Gautam Srivastava. “Consumer recommendation prediction in online reviews using Cuckoo optimized machine learning models”. Comput. Electr. Eng. 95 107397, (2021). [CrossRef] [Google Scholar]
  8. Moro S, Rita P, Coelho J. Stripping customers’ feedback on hotels through data mining: The case of las vegas strip. Tour. Manag. Perspect. 23:41–52, (2017). [Google Scholar]
  9. Mika V. Mäntylä, Daniel Graziotin, Miikka Kuutila. The evolution of sentiment analysis-A review of research topics, venues, and top cited papers. Comput. Sci. Rev. 27, 16–32, (2018). [CrossRef] [Google Scholar]
  10. Ligthart, Alexander. C. Catal, B. Tekinerdogan. Systematic reviews in sentiment analysis: a tertiary study. Artif. Intell. Rev. 54:4997–5053, (2021). [CrossRef] [Google Scholar]
  11. Rustam, Furqan. Imran Ashraf, Arif Mehmood, Saleem Ullah, Gyu Sang Choi. Tweets Classification on the Base of Sentiments for US Airline Companies. Entropy, 21, 1078, (2019). [CrossRef] [Google Scholar]
  12. Sternberg, F., Hedegaard Pedersen, K., Ryelund, N. K., Mukkamala, R. R., Vatrapu, R. “Analysing Customer Engagement of Turkish Airlines Using Big Social Data”. 2018 IEEE International Congress on Big Data (Big Data Congress), (2018). [Google Scholar]
  13. Rane A, Kumar A. ”Sentiment classification system of Twitter data for US airline service analysis.” In: IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC). 1, IEEE; p. 769–73, (2018). [Google Scholar]
  14. Kumar, Sachin. Mikhail Zymbler. “A machine learning approach to analyze customer satisfaction from airline tweets”. J. Big Data 6, 1 62, (2019). [CrossRef] [Google Scholar]
  15. Jain, Praphula Kumar. Vijayalakshmi Saravanan, Rajendra Pamula. “A Hybrid CNN-LSTM: A Deep Learning Approach for Consumer Sentiment Analysis Using Qualitative User-Generated Contents”. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 20, 5, Article 84, 15 pages, July (2021). [CrossRef] [Google Scholar]
  16. Tan, K.L., Lee, C.P., Lim, K.M. A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research. Appl. Sci. (2023), 13, 4550. [CrossRef] [Google Scholar]
  17. Breiman, L.E.O. Random Forests. Mach. Learn. 45, p.5-32, (2001). [CrossRef] [Google Scholar]
  18. N. Chawla, K. Bowyer, L. Hall, W. Kegelmeyer, “SMOTE: Synthetic Minority Over-Sampling Technique,” J. Artif. Intell. Res., vol. 16, p. 321–357, (2002). [CrossRef] [Google Scholar]
  19. J. Ah-Pine, E. P. S. Morales, “A Study of Synthetic Oversampling for Twitter Imbalanced Sentiment Analysis ", Proceedings of the Workshop on Interactions between Data Mining and Natural Language Processing, DMNLP, (2016). [Google Scholar]
  20. Allen, J., Liu, H., Iqbal, S., Zheng, D., Stansby, G. Deep learning-based photoplethysmography classification for peripheral arterial disease detection: A proof-of-concept study. Physiol. Meas. 42(5), (2021). [Google Scholar]
  21. Prusty S, Patnaik S, Dash SK. SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer. Front. Nanotechnol. 4:972421, (2022). [CrossRef] [Google Scholar]
  22. Patel, Aksh. Parita Oza, Smita Agrawal. Sentiment Analysis of Customer Feedback and Reviews for Airline Services using Language Representation Model. Procedia Comput. Sci. 218 2459–2467, (2023). [CrossRef] [Google Scholar]
  23. Kumar, Pradeep. Roheet Bhatnagar, Kuntal Gaur, Anurag Bhatnagar. Classification of Imbalanced Data:Review of Methods and Applications. IOP Conf. Series: Materials Science and Engineering 1099 012077, (2021). [CrossRef] [Google Scholar]
  24. Jain, Praphula Kumar. Rajendra Pamula, Gautam Srivastava. “A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews”. Comput. Sci. Rev. 41 100413, (2021). [CrossRef] [Google Scholar]
  25. Alzamzami, Fatimah. M. Hoda, A. El Saddik. “Light Gradient Boosting Machine for General Sentiment Classification on Short Texts: A Comparative Evaluation”. IEEE Access. May, (2020). [Google Scholar]
  26. Fatemeh Hemmatian, Mohammad Karim Sohrabi. A survey on classification techniques for opinion mining and sentiment analysis. Artif. Intell. Rev. 52, 1495–1545, (2019). [CrossRef] [Google Scholar]

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