Skip to main content
SpringerLink
Log in

A multi-label ensemble predicting model to service recommendation from social media contents

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Consumer sentiment is one of the essential measures of predictive recommendations in travel and tourism. Nowadays, a massive amount of data is available on the online platform related to consumer sentiment, which may help draw insights into how consumers provide feedback and how we can use that feedback to predict recommendations using machine learning techniques. In this study, we have designed a predictive recommendations method that predicts the consumer recommendations in travel and tourism, particularly in the case of the airline. We developed our predictive methods as a multi-label classification system. We implemented K-Nearest Neighbors, Support Vector Machine, Multi-layer Perceptron, Logistic Regression, Random Forest, and Ensemble Learning as basic classification models to train our model. Further, we boosted our predictive model by implementing state-of-the-art partitioning methods to partition the label space in lower spaces and utilized label space partitioning approaches, namely RAkELo and Louvain, as a transformation technique to transform every label set into a multi-class classification problem. The suggested model obtained higher performance in terms of accuracy using various evaluation measures compared to other binary classifications. Furthermore, the valuable traveler may get benefited from this approach in making their predictive decisions before travel.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. 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 

  2. 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 

  3. Guan Y, Wei Q, Chen G (2019) Deep learning based personalized recommendation with multi-view information integration. Decis Support Syst 118:58–69

    Article  Google Scholar 

  4. Koehn D, Lessmann S, Schaal M (2020) Predicting online shopping behaviour from clickstream data using deep learning. Expert Syst Appl 150:113342

    Article  Google Scholar 

  5. Kumar A et al (2019) Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention. Neural Comput Appl 312:877–890

    Article  Google Scholar 

  6. Guerreiro J, Rita P (2019) How to predict explicit recommendations in online reviews using text mining and sentiment analysis. J Hosp Tour Manag 43:269

    Article  Google Scholar 

  7. 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 

  8. Guo Y, Wang M, Li X (2017) Application of an improved Apriori algorithm in a mobile e-commerce recommendation system. Ind Manag Data Syst. https://doi.org/10.1108/IMDS-03-2016-0094/full/html

    Article  Google Scholar 

  9. Schneider MJ, Gupta S (2016) Forecasting sales of new and existing products using consumer reviews: a random projections approach. Int J Forecast 32(2):243–256

    Article  Google Scholar 

  10. Qazi A et al (2017) Assessing consumers satisfaction and expectations through online opinions: expectation and disconfirmation approach. Comput Hum Behav 75:450–460

    Article  Google Scholar 

  11. Jain K, 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 

  12. Ye Q, Zhang Z, Law R (2009) Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst Appl 36(3):6527–6535

    Article  Google Scholar 

  13. Lee HA, Rob L, Jamie M (2011) Helpful reviewers in TripAdvisor, an online travel community. J Travel Tourism Market 287:675–688

    Article  Google Scholar 

  14. Jain PK, Vijayalakshmi S, Rajendra P (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 205:1–15

    Google Scholar 

  15. Jain PK et al (2021) SpSAN: sparse self-attentive network-based aspect-aware model for sentiment analysis. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-021-03436-x

    Article  Google Scholar 

  16. Jain PK et al (2019) Airline recommendation prediction using customer generated feedback data. In: 2019 4th International Conference on Information Systems and Computer Networks (ISCON). IEEE

  17. Yekun EA, Abrahaley T (2019) Student performance prediction with optimum multilabel ensemble model. arXiv preprint arXiv:1909.07444

  18. Guo Y, Barnes SJ, Jia Q (2017) Mining meaning from online ratings and reviews: tourist satisfaction analysis using latent dirichlet allocation. Tour Manag 59:467–483

    Article  Google Scholar 

  19. Gavilan D, Avello M, Martinez-Navarro G (2018) The influence of online ratings and reviews on hotel booking consideration. Tour Manag 66:53–61

    Article  Google Scholar 

  20. Yue L et al (2019) A survey of sentiment analysis in social media. Knowledge Inf Syst 60:617

    Article  Google Scholar 

  21. Phillips L et al (2017) Using social media to predict the future: a systematic literature review. arXiv preprint arXiv:1706.06134

  22. Çalı S, Balaman ŞYı (2019) Improved decisions for marketing, supply and purchasing: mining big data through an integration of sentiment analysis and intuitionistic fuzzy multi criteria assessment. Comput Ind Eng 129:315–332

    Article  Google Scholar 

  23. Reichheld FF (2003) The one number you need to grow. Harvard Bus Rev 81(12):46–55

    Google Scholar 

  24. Malik MSI, Hussain A (2017) Helpfulness of product reviews as a function of discrete positive and negative emotions. Comput Human Behav 73:290–302

    Article  Google Scholar 

  25. Li H, Liu H, Zhang Z (2020) Online persuasion of review emotional intensity: a text mining analysis of restaurant reviews. Int J Hosp Manag 89:102558

    Article  Google Scholar 

  26. Chen Z, Yuan M (2020) Psychology of word of mouth marketing. Curr Opin Psychol 31:7–10

    Article  Google Scholar 

  27. Moore SG, Lafreniere KC (2020) How online word-of-mouth impacts receivers. Consum Psychol Rev 3(1):34–59

    Article  Google Scholar 

  28. Yin D, Bond SD, Zhang H (2014) Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Q 38(2):539–560

    Article  Google Scholar 

  29. Chowdhary KR (2020) Natural language processing. Fundamentals of artificial intelligence. Springer, pp 603–649

  30. Church KW (2017) Word2Vec. Nat Lang Eng 23(1):155–162

    Article  Google Scholar 

  31. Deng X, Yenchun Jim W, Zhuang F (2020) Trust-embedded collaborative deep generative model for social recommendation. J Supercomput 76(11):8801–8829

    Article  Google Scholar 

  32. Mohtavipour SM, Shahhoseini HS (2020) A link-elimination partitioning approach for application graph mapping in reconfigurable computing systems. J Supercomput 76(1):726–754

    Article  Google Scholar 

  33. Zhang N, Jia Z, Shi Z (2005) Text categorization with KNN algorithm. Comput Eng 8

  34. Vishwanathan SVM, Narasimha Murty M (2002) SSVM: a simple SVM algorithm. In: Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No. 02CH37290). Vol. 3. IEEE

  35. Rathbun TF et al (1997) MLP iterative construction algorithm. Neurocomputing 17(3–4):195–216

    Article  Google Scholar 

  36. Kleinbaum DG et al (2002) Logistic regression. Springer, New York

    MATH  Google Scholar 

  37. Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22

    Google Scholar 

  38. Polikar R (2012) Ensemble learning. Ensemble machine learning. Springer, Boston, MA, pp 1–34

  39. Danesh M, Dorrigiv M, Yaghmaee F (2021) Ensemble-based clustering of large probabilistic graphs using neighborhood and distance metric learning. J Supercomput 77(4):4107–4134

    Article  Google Scholar 

  40. Tsoumakas G, Vlahavas I (2007) Random k-labelsets: an ensemble method for multilabel classification. In: European Conference on Machine Learning. Springer, Berlin, Heidelberg

  41. Dembczyński K et al (2012) On label dependence and loss minimization in multi-label classification. Mach Learn 88(1–2):5–45

    Article  MathSciNet  Google Scholar 

  42. Clare A, Ross DK (2001) Knowledge discovery in multi-label phenotype data. In: European Conference on Principles of Data Mining And Knowledge Discovery. Springer, Berlin, Heidelberg

  43. Szymański P, Kajdanowicz T, Kersting K (2016) How is a data-driven approach better than random choice in label space division for multi-label classification? Entropy 18(8):282

    Article  Google Scholar 

  44. Newman MEJ (2004) Analysis of weighted networks. Phys Rev E 70(5):056131

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, JH, 826004, India

    Praphula Kumar Jain & Rajendra Pamula

  2. School of Electrical and Computer Engineering, Ethiopian Institute of Technology (EiT - M), Mekelle University (MU), Mekele, Ethiopia

    Ephrem Admasu Yekun

Authors
  1. Praphula Kumar Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajendra Pamula
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ephrem Admasu Yekun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Praphula Kumar Jain.

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., Pamula, R. & Yekun, E.A. A multi-label ensemble predicting model to service recommendation from social media contents. J Supercomput 78, 5203–5220 (2022). https://doi.org/10.1007/s11227-021-04087-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-04087-7

Keywords

Search

Navigation