Customers differ greatly in their demographics, lifestyles, needs, perceptions, preferences, and behaviors. A business entity needs to understand the profitability of customers in a segment and their potential lifetime profitability. In this paper, our focus is on applying sentiment analysis to analyze feedback of passengers obtained from the airline forum. For this purpose, Multinomial Naive Bayes and Linear Support Vector models are used. Training data consisted of 1217 positive reviews and 955 negative reviews. Sentiments were predicted for 868 reviews. This work also aims to find a suitable data model that achieves a high accuracy and dependence on various pre-processing approaches. The sentiment analysis result is plotted as a bar graph visualization and evaluated against the overall trip rating obtained from the forum. We want to help the airline industry maximize the delivery and service to meet customer expectations and build customer loyalty.

1. B. Pang, L. Lee and S. Vaithyanathan, “Thumbs up? sentiment classification using machine learning techniques,” in Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, Association for Computational Linguistics, Stroudsburg, PA, pp. 79-86, 2002.
2. K. Yessenov and S. Misailovic, “Sentiment analysis of movie review comments,” Unpublished dissertation, Massachusetts Institute of Technology, Cambridge, MA, 2009.
3. A. Agarwal, B. Xie, I. Vovsha, O. Rambow and R. Passonneau, “Sentiment analysis of twitter data,” in Proceedings of the Workshop on Languages in Social Media, Association for Computational Linguistics, Stroudsburg, PA, pp. 30-38, 2011.
4. E. Adeborna and K. Siau, “An approach to sentiment analysis-the case of airline quality rating,” in Pacific Asia Conference on Information Systems PACIS, Chengdu, China, 2014, p. 363.
5. V. Balakrishnan and E. Lloyd-Yemoh,“ Stemming and lemmatization: A comparison of retrieval performances,” Lecture Notes on Software Engineering, vol. 2, no. 3, pp. 262-267, 2014. https://dx.doi.org/10.7763/LNSE.2014.V2.134
6. P. Bhattacharyya, A. Bahuguna, L. Talukdar and B. Phukan, “Facilitating multi-lingual sense annotation: Human mediated lemmatizer,” in Global WordNet Conference, Tartu, Estonia, 2014.
7. S. S. Megala, A. Kavitha and A. Marimuthu, “Improvised stemming algorithm-TWIG,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 7, pp. 168-171, 2013.
8. C. D. Manning, P. Raghavan and H. Schutze, Introduction to Information Retrieval, New York, NY: Cambridge University Press, 2008.
9. SAS. (n.d). Data visualization, what it is and why it matters [Online]. Available: https://goo.gl/4GUUov
10. SKYTRAX (n.d). Airline rating and reviews [Online]. Available: https://www.airlinequality.com/

A. X. Annie R, V. Mohan and S. H. Venu, “Sentiment analysis applied to airline feedback to boost customers’ endearment,” International Journal of Applied and Physical Sciences, vol. 2, no. 2, pp. 51-58, 2016.