Skip to main content
Log in

Predicting airline customers’ recommendations using qualitative and quantitative contents of online reviews

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Customers generally give ratings and reviews for different services that they get online or offline. These reviews and ratings aspects are effectively helpful to both the company and customers to receive feedback and make the right decisions, respectively. However, the number of reviews and ratings can increase exponentially, bringing a new challenge for the company to manage and track. Under these circumstances, it will also be hard for the customer to make the right decision. In this work, we summarize text reviews and ratings given by passengers for different airlines. The objective of this research is to predict whether the recommendation made by the customer is positive or negative. Two types of features, namely, textual feature and explicit ratings, are extracted from the dataset and other attributes. We found the relationship between such sentiments and feelings expressed in online reviews and predictive consumer recommendation decisions. We have considered quantitative content with qualitative content of online reviews in predicting recommendation decisions, which shows the work’s novelty. Additionally, the obtained results yield an essential contribution to the existing literature in terms of service evaluation, making managerial policies, and predictive consumer recommendations, etc. Moreover, we hope that this work would be helpful for practitioners who wish to utilize the technique to make the quick and essential hidden information by combining textual reviews and various service aspects ratings.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Arndt J (1967) Role of product-related conversations in the diffusion of a new product. J Mark Res 4(3):291–295

    Article  Google Scholar 

  2. Ayeh JK, Au N, Law R (2013) “do we believe in tripadvisor?” examining credibility perceptions and online travelers’ attitude toward using user-generated content. J Travel Res 52(4):437–452

    Article  Google Scholar 

  3. Braun ML, Buhmann JM, MÞller K-R (2008) On relevant dimensions in kernel feature spaces. J Mach Learn Res 9:1875–1908

    MathSciNet  MATH  Google Scholar 

  4. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  5. Cooley CH (1983) Social organization. Transaction Publishers

  6. Gupta S, Zeithaml V (2006) Customer metrics and their impact on financial performance. Mark Sci 25(6):718–739

    Article  Google Scholar 

  7. Chevalier JA, Mayzlin D (2006) The effect of word of mouth on sales: Online book reviews. J Mark Res 43(3):345–354

    Article  Google Scholar 

  8. Cheung CM-Y, Sia C-L, Kuan KK (2012) Is this review believable? a study of factors affecting the credibility of online consumer reviews from an elm perspective. J Assoc Inf Syst 13(8):618

    Google Scholar 

  9. Chatterjee S (2019) Explaining customer ratings and recommendations by combining qualitative and quantitative user generated contents. Decis Support Syst 119:14–22

    Article  Google Scholar 

  10. Hastie T, Tibshirani R, Friedman J, Franklin J (2005) The elements of statistical learning: data mining, inference and prediction. Math Intell 27 (2):83–85

    Google Scholar 

  11. Jain PK, Pamula R (2020) Sentiment analysis in airline data: Customer rating based recommendation prediction using weka. In: Machine learning algorithms for industrial applications. Springer, pp 53–65

  12. Jain PK, Pamula R, Srivastava G (2021) A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews. Comput Sci Rev 41:100413

    Article  Google Scholar 

  13. Jain PK, Saravanan V, Pamula R (2021) A hybrid cnn-lstm: a deep learning approach for consumer sentiment analysis using qualitative user-generated contents. Trans Asian Low-Resour Lang Inf Process 20(5):1–15

    Article  Google Scholar 

  14. Jain PK, Pamula R, Ansari S, Sharma D, Maddala L (2019) Airline recommendation prediction using customer generated feedback data. In: 2019 4th International Conference on Information Systems and Computer Networks (ISCON). IEEE, pp 376–379

  15. Jain PK, Quamer W, Pamula R, Saravanan V (2021) Spsan: Sparse self-attentive network-based aspect-aware model for sentiment analysis. J Ambient Intell Humaniz Comput :1–18

  16. Jang S, Prasad A, Ratchford BT (2012) How consumers use product reviews in the purchase decision process. Mark Lett 23(3):825–838

    Article  Google Scholar 

  17. Keiningham TL, Cooil B, Aksoy L, Andreassen TW, Weiner J (2007) The value of different customer satisfaction and loyalty metrics in predicting customer retention, recommendation, and share-of-wallet. Manag Serv Qual Int J 17(4):361–384

    Article  Google Scholar 

  18. Keiningham TL, Cooil B, Andreassen TW, Aksoy L (2007) A longitudinal examination of net promoter and firm revenue growth. J Mark 71(3):39–51

    Article  Google Scholar 

  19. Korfiatis N, GarcíA-Bariocanal E, SáNchez-Alonso S (2012) Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content. Electron Commer Res Appl 11(3):205–217

    Article  Google Scholar 

  20. Kohavi R, Quinlan JR (2002) Data mining tasks and methods: Classification: decision-tree discovery. In: Handbook of data mining and knowledge discovery, Oxford University Press, Inc., pp 267–276

  21. Kuan KK, Hui K-L, Prasarnphanich P, Lai H-Y (2015) What makes a review voted? an empirical investigation of review voting in online review systems. J Assoc Inf Syst 16(1):48

    Google Scholar 

  22. Kusumasondjaja S, Shanka T, Marchegiani C (2012) Credibility of online reviews and initial trust: The roles of reviewer’s identity and review valence. J Vacat Mark 18(3):185–195

    Article  Google Scholar 

  23. Lis B, Neßler C (2014) Electronic word of mouth. Bus Inf Syst Eng 6(1):63–65

    Article  Google Scholar 

  24. Liang X, Yang Y (2018) An experimental study of chinese tourists using a company-hosted wechat official account. Electron Commer Res Appl 27:83–89

    Article  Google Scholar 

  25. Lucini FR, Tonetto LM, Fogliatto FS, Anzanello MJ (2020) Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews. J Air Transp Manag 83:101760

    Article  Google Scholar 

  26. Muhammad SS, Dey BL, Weerakkody V (2018) Analysis of factors that influence customers’ willingness to leave big data digital footprints on social media: A systematic review of literature. Inf Syst Front 20(3):559–576

    Article  Google Scholar 

  27. Punel A, Hassan LAH, Ermagun A (2019) Variations in airline passenger expectation of service quality across the globe. Tour Manage 75:491–508

    Article  Google Scholar 

  28. Preko A, Agbanu SK, Feglo M (2014) Service delivery, customer satisfaction and customer delight in the real estate business. evidence from elite kingdom investment and consulting company ghana. Eur J Bus Manag 6(3):71–83

    Google Scholar 

  29. Quamer W, Jain PK, Rai A, Saravanan V, Pamula R, Kumar C (2021) Sacnn: Self-attentive convolutional neural network model for natural language inference. Trans Asian Low-Resour Lang Inf Process 20(3):1–16

    Article  Google Scholar 

  30. Reichheld FF (2004) The one number you need to grow. Harv Bus Rev 82(6):133–133

    Google Scholar 

  31. Reichheld FF, Markey R (2011) The ultimate question 2.0: How net promoter companies thrive in a customer-driven world. Harvard Business Press

  32. Richins ML, Root-Shaffer T The role of evolvement and opinion leadership in consumer word-of-mouth: An implicit model made explicit, ACR North American Advances

  33. Richins ML (1983) Negative word-of-mouth by dissatisfied consumers: A pilot study. J Mark 47(1):68–78

    Article  Google Scholar 

  34. Salton G, Wong A, Yang C-S (1975) A vector space model for automatic indexing. Commun ACM 18(11):613–620

    Article  Google Scholar 

  35. Sezgen E, Mason KJ, Mayer R (2019) Voice of airline passenger: A text mining approach to understand customer satisfaction. J Air Transp Manag 77:65–74

    Article  Google Scholar 

  36. Siering M, Deokar AV, Janze C (2018) Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews. Decis Support Syst 107:52–63

    Article  Google Scholar 

  37. Sparks BA, Browning V (2011) The impact of online reviews on hotel booking intentions and perception of trust. Tour Manag 32(6):1310–1323

    Article  Google Scholar 

  38. Tamrakar CB, Pyo T-H, Gruca TS (2018) Social media sentiment and firm value

  39. Verma VK, Chandra B (2018) Sustainability and customers’ hotel choice behaviour: a choice-based conjoint analysis approach. Environ Dev Sustain 20(3):1347–1363

    Article  Google Scholar 

  40. Vermeulen IE, Seegers D (2009) Tried and tested: The impact of online hotel reviews on consumer consideration. Tour Manag 30(1):123–127

    Article  Google Scholar 

  41. Zhang W, Yoshida T, Tang X (2011) A comparative study of tf* idf, lsi and multi-words for text classification. Expert Syst Appl 38(3):2758–2765

    Article  Google Scholar 

  42. Zhu F, Zhang X (2010) Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. J Mark 74(2):133–148

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Praphula Kumar Jain.

Ethics declarations

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this article.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jain, P.K., Patel, A., Kumari, S. et al. Predicting airline customers’ recommendations using qualitative and quantitative contents of online reviews. Multimed Tools Appl 81, 6979–6994 (2022). https://doi.org/10.1007/s11042-022-11972-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-11972-7

Keywords

Navigation