Skip to main content

Advertisement

SpringerLink
Log in

A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews

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

Abstract

This paper investigates the sentiment and emotion of digital payment application consumers using a hybrid approach consisting of both supervised and unsupervised machine learning techniques. Support vector machine, random forest and Naïve Bayes were modeled for sentiment and emotion analyses, whereas latent Dirichlet allocation was administered to identify top emerging topics based on English textual reviews from three digital payment applications. Random forest produced the best results for sentiment (F1 score = 73.8%; Cohen’s Kappa = 52.2%) and emotion (F1 score = 58.8%; Cohen’s Kappa = 44.7%) analyses based on a tenfold cross-validation. Latent Dirichlet allocation revealed best clusters at k = 5 and items = 25, with the top topics being App Service, Transaction, Reload Features, Connectivity and Reward. Findings are presented and discussed in general and also based on each application.

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
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Jain M, Singla R (2017) Digital wallet: a handy solution in the wake of Demonetisation. Biz Bytes 8(1):188–197

    Google Scholar 

  2. Zhang L, Chen S (2019) China’s digital economy: opportunities and risks. https://www.imf.org/en/Publications/WP/Issues/2019/01/17/Chinas-Digital-Economy-Opportunities-and-Risks-46459 (Accessed November 25, 2019)

  3. Kharpal A (2019) Alibaba breaks Singles Day record with more than $38 billion in sales. https://www.cnbc.com/2019/11/11/alibaba-singles-day-2019-record-sales-on-biggest-shopping-day.html (Accessed December 2, 2019)

  4. Kaur W, Balakrishnan V (2018) Improving sentiment scoring mechanism: a case study on airline services. Ind Manag Data Syst 118(8):1578–1596

    Article  Google Scholar 

  5. Li G (2017) Application of sentiment analysis: assessing the reliability and validity of the global airlines rating program. In: 9th IBA Bachelor Thesis Conference, Enschede, The Netherlands, pp 1–12

  6. Yordanova S, Kabakchieva D (2017) Sentiment classification of hotel reviews in social media with decision tree learning. Int J Comput Appl 158:1–7

    Google Scholar 

  7. Palkar RK, Gala KD, Shah MM, Shah JN (2016) Comparative evaluation of supervised learning algorithms for sentiment analysis of movie reviews. Int J Comput Appl 142:20–26

    Google Scholar 

  8. Lee H, Lee N, Seo H et al (2020) Developing a supervised learning-based social media business sentiment index. J Supercomput 76:3882–3897

    Article  Google Scholar 

  9. BalaAnand M, Karthikeyan N, Karthik S et al (2019) An enhanced graph-based semi-supervised learning algorithm to detect fake users on Twitter. J Supercomput 75:6085–6105

    Article  Google Scholar 

  10. Balakrishnan V, Marian CM, Wandeep K, Amir J (2019) A comparative analysis of detection mechanisms for emotion detection. In: Proceedings of International Conference on Computer Science and Engineering: Industrial Revolution 4.0 Opportunities and Challenges, p 23, April 26–27, Padang, Indonesia

  11. Panwar S, Rad P, Choo KR et al (2019) Are you emotional or depressed? Learning about your emotional state from your music using machine learning. J Supercomput 75:2986–3009

    Article  Google Scholar 

  12. Huq MR, Ali A, Rahman A (2017) Sentiment analysis on Twitter data using KNN and SVM. Int J Adv Comput Sci Appl 8(6):19–25

    Google Scholar 

  13. Ray P, Chakrabarti A, Ganguli B, Das PK (2018) Demonetization and its aftermath: an analysis based on Twitter sentiments. Sādhanā 43(11):186–201

    Article  Google Scholar 

  14. Maindola P, Singhal N, Dubey AD (2018) Sentiment analysis of digital wallets and UPI systems in India post demonetization using IBM Watson. In: 2018 International Conference on Computer Communication and Informatics (ICCCI), pp 1–6. IEEE

  15. Mun YP, Khalid H, Nadarajah D (2017) Millennials’ perception on mobile payment services in Malaysia. Proc Comput Sci 124:397–404

    Article  Google Scholar 

  16. Zurena RS, Mohd SMZ, Ida RO (2013) Responsiveness of smart card in restaurants: factor analysis approach. Proc Soc Behav Sci 105:745–754

    Article  Google Scholar 

  17. Ligon E, Malick B, Sheth K, Trachtman C (2019) What explains low adoption of digital payment technologies? Evidence from small-scale merchants in Jaipur, India. PloS One. https://doi.org/10.1371/journal.pone.0219450

    Article  Google Scholar 

  18. Reiss DG (2018) Is money going digital? An alternative perspective on the current hype. Financial Innov 4(1):14–22

    Article  Google Scholar 

  19. Clement J (2019) Mobile payments worldwide—statistics and facts. https://www.statista.com/topics/4872/mobile-payments-worldwide/ (Accessed Oct 10, 2019)

  20. Nizam F, Hwang HJ, Valaei N (2018) Measuring the effectiveness of E-wallet in Malaysia. In: 3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science Engineering, pp 59–69. Springer, Cham

  21. Varsha R, Thulasiram M (2016) Acceptance of e-wallet services: a study of consumer behavior. Int J Innov Res Manag Stud 1(4):133–141

    Google Scholar 

  22. Malaysian Communications and Multimedia Commission (2018) Internet Users Survey 2018. https://www.mcmc.gov.my/en/resources/statistics/internet-users-survey (Accessed November 10, 2019)

  23. Aziz A (2019) Budget 2020: One-time RM30 e-wallet stimulus from Govt will greatly drive adoption—Boost. The Edge Markets. https://www.theedgemarkets.com/article/onetime-rm30-ewallet-stimulus-govt-will-greatly-drive-adoption-%E2%80%94-boost

  24. Wycech S (2015) An Investigation of Attitudes towards Mobile Payments, MSc in Management of Information Systems, University of Dublin

  25. Giachanou A, Crestani F (2016) Like it or not: a survey of twitter sentiment analysis methods. ACM Comput Surv CSUR. https://doi.org/10.1145/2938640

    Article  Google Scholar 

  26. Apte P, Khetwat SS (2018) Text-based emotion analysis: feature selection techniques and approaches. Emerging Technologies in Data Mining and Information Security, pp 837–847

  27. Henrique BM, Sobreiro VA, Kimura H (2018) Stock price prediction using support vector regression on daily and up to the minute prices. J Finance Data Sci 3:183–201

    Article  Google Scholar 

  28. Esparza GG, de-Luna A, Zezzatti AO, Hernandez A, Ponce J, Álvarez M, de Jesus Nava J (2017) A Sentiment Analysis Model to Analyze Students Reviews of Teacher Performance Using Support Vector Machines. In: Omatu S, Rodríguez S, Villarrubia G, Faria P, Sitek P, Prieto J (eds) Distributed Computing and Artificial Intelligence, 14th International Conference, Advances in Intelligent Systems and Computing, vol 620. Springer, Cham

  29. Korovkinas K, Danėnas P, Garšva G (2019) SVM and k-means hybrid method for textual data sentiment analysis. Baltic J Mod Comput 7(1):47–60

    Article  Google Scholar 

  30. Al Amrani Y, Lazaar M, El Kadiri KE (2018) Random forest and support vector machine based hybrid approach to sentiment analysis. Proc Comput Sci 127:511–520

    Article  Google Scholar 

  31. Deshpande M, Rao V (2017) Depression detection using emotion artificial intelligence. In: the 2017 International Conference on Intelligent Sustainable Systems (ICISS). Palladam, India. https://ieeexplore.ieee.org/document/8389299

  32. Baid P, Gupta A, Chaplot N (2017) Sentiment analysis of movie reviews using machine learning techniques. Int J Comput Appl 179(7):45–49

    Google Scholar 

  33. Westland JC (2017) Methods to assess the value of new technologies: the case of consumer sentiment towards digital wallet technology. Data-Enabled Discov Appl. https://doi.org/10.1007/s41688-017-0003-0

    Article  Google Scholar 

  34. Anagnostopoulos C.-N, Iliou T, Giannoukos I (2012) Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011. In: Springer Science+ Business Media, Dordrecht

  35. Kahou SE, Bouthillier X, Lamblin P, Gulcehre C, Michalski V, Konda K, Boulanger-Lewandowski N (2016) Emonets: Multimodal deep learning approaches for emotion recognition in video. J Multimod User Interfaces 10(2):99–111

    Article  Google Scholar 

  36. Martino DJ, Samamé C, Strejilevich SA (2016) Stability of facial emotion recognition performance in bipolar disorder. Psych Res 243:182–184

    Article  Google Scholar 

  37. Sailunaz K, Dhaliwal M, Rokne J, Alhajj R (2018) Emotion detection from text and speech: a survey. Soc Netw Anal Min. https://doi.org/10.1007/s13278-018-0505-2

    Article  Google Scholar 

  38. Sarakit P, Theeramunkong T, Haruechaiyasak C, Okumura M (2015) Classifying emotion in Thai Youtube comments. In: The 2015 6th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES), Hua-Hin, Thailand. https://ieeexplore.ieee.org/document/7110808

  39. Ekman P (1992) An argument for basic emotions. Cogn Emot 6(3–4):169–200

    Article  Google Scholar 

  40. Plutchik R (1980) Emotion: Theory, Research, and Experience, Volume 1: Theories of Emotion. Michigan: Academic Press

  41. Wang X, Tang LR, Kim E (2019) More than words: Do emotional content and linguistic style matching matter on restaurant review helpfulness? Int J Hosp Manag 77:438–447

    Article  Google Scholar 

  42. Oktadiana H, Pearce PL (2018) Asean tourism slogans: assessing emotional distinctiveness through lexical database analysis. In: Newcastle NSW (ed) CAUTHE 2018: Get Smart: Paradoxes and Possibilities in Tourism, Hospitality and Events Education and Research. Newcastle Business School, The University of, Newcastle, pp 200–214

    Google Scholar 

  43. Ficamos P, Liu Y (2016) A Topic based approach for sentiment analysis on twitter data. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2016.071226

    Article  Google Scholar 

  44. Roelleke T, Wang J (2008) TF-IDF uncovered: a study of theories and probabilities. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Singapore

  45. Chawla N, Bowyer K, Hall L, Kegelmeyer W (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

    Article  Google Scholar 

  46. Hossain R, Ibrahim R, Dollah RB, Zain M, Anwar K, Khaidzir M (2017) Experimental study of support vector machines and Naïve Bayes classifier on automated subject area classification. J Inf Syst Res Innov 11(3):7–13

    Google Scholar 

  47. Kharde VA, Sonawane SS (2016) Sentiment analysis of twitter data: a survey of techniques. Int J Comput Appl 139(11):5–15

    Google Scholar 

  48. Ahmad M, Aftab S, Muhammad SS, Ahmad S (2017) Machine learning techniques for sentiment analysis: a review. Int J Multidiscip Sci Eng 8(3):27–32

    Google Scholar 

  49. Dani H, Li J, Liu H (2017) Sentiment informed cyberbullying detection in social media. In: The Joint European Conference on Machine Learning and Knowledge Discovery in Databases. https://ecmlpkdd2017.ijs.si/papers/paperID25.pdf

  50. Powers DM (2011) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J Mach Learn Technol 2(1):37–63

    MathSciNet  Google Scholar 

  51. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46

    Article  Google Scholar 

  52. Fang X, Zhan J (2015) Sentiment analysis using product review data. J Big Data. https://doi.org/10.1186/s40537-015-0015-2

    Article  Google Scholar 

  53. D’Andrea A, Ferri F, Grifoni P, Guzzo T (2015) Approaches, tools and applications for sentiment analysis implementation. Int J Comput Appl 5(3):26–33

    Google Scholar 

Download references

Acknowledgements

The authors extend their gratitude to the Ministry of Education for supporting this study: [Fundamental Research Grant System: FP109 – 2018A].

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia

    Vimala Balakrishnan

  2. The Nielsen Company, Kuala Lumpur, Malaysia

    Pik Yin Lok

  3. School of Humanities, Universiti Sains Malaysia, Penang, Malaysia

    Hajar Abdul Rahim

Authors
  1. Vimala Balakrishnan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pik Yin Lok
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hajar Abdul Rahim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vimala Balakrishnan.

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

Balakrishnan, V., Lok, P.Y. & Abdul Rahim, H. A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews. J Supercomput 77, 3795–3810 (2021). https://doi.org/10.1007/s11227-020-03412-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03412-w

Keywords

Search

Navigation