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Recognizing factors effecting the use of mobile banking apps through sentiment and thematic analysis on user reviews

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Abstract

We live in an age where the use of smart devices and Internet are redefining our community standards. Additionally, the pandemic Covid-19 enforced the community to use applications on smart devices for various activities. Currently, many organizations are developing their applications that are accessible through various platforms, including Windows Phone Store, Apple App Store, and Google Play. To facilitate the customer the banking sector is also providing their mobile applications for various online services. Mobile banking applications (mbanking apps) have considerably upgraded the efficiency of the banks and living standards of the people. The people can easily download applications from app stores and are permitted to leave reviews or comments on the mobile application. The sentiment analysis is an area that allows us to examine the user opinion to improve the online services. Therefore, for any organization it is of prime importance to explore and evaluate the weaknesses affecting the delivery of their online services. In this work, sentiment analysis is performed to evaluate ten (10) mbanking apps of Pakistan using valence aware dictionary for sentiment reasoning and machine learning (ML) based approaches. Performance of three classifiers through supervised ML techniques multinomial Naïve Bayes, logistic regression, support vector machine, and ensemble model is compared and employed. Moreover, the thematic analysis of reviews is also performed to discover various factors as themes that affect the effectiveness of the mbanking apps by using Top2Vec Model. The results indicate that the ensemble model is best performing model with f1-score of 90%. The thematical analysis uncovers 346 positive themes like ease of use, helpful, reliable, user friendly, good aesthetics, convenience, secured and many more, whereas 441 negative themes comprise performance issue, poor updates/new version in apps, account registration issue, app crash problem, etc.

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Data availability

The data used to support the study's findings are available from the corresponding author upon request.

Notes

  1. https://www.microsoft.com.

  2. https://www.apple.com/ios/app-store/.

  3. https://www.play.google.com/store/apps.

References

  1. Ghasiya P, Okamura K (2021) Investigating COVID-19 news across four nations: a topic modeling and sentiment analysis approach. IEEE Access 9:36645–36656

    Article  Google Scholar 

  2. Martens D, Maalej W (2019) Towards understanding and detecting fake reviews in app stores. Empir Softw Eng 24:3316–3355

    Article  Google Scholar 

  3. Naseem S, Mahmood T, Asif M, Rashid J, Umair M, Shah M (2021) Survey on sentiment analysis of user reviews. In: 2021 international conference on innovative computing (ICIC), pp 1–6

  4. Singh J, Singh G, Singh R (2017) Optimization of sentiment analysis using machine learning classifiers. HCIS 7:1–12

    Google Scholar 

  5. Zafar M, Zaheer A, ur Rehman K (2011) Impact of online service quality on customer satisfaction in banking sector of Pakistan. Afr J Bus Manag 5:11786

    Google Scholar 

  6. Haroon A, Mahmood T, Ashraf R, Asif M, Naseem S, Khan AW (2021) A comprehensive survey of sentiment analysis based on user opinion. In: 2021 4th international conference on computing & information sciences (ICCIS), pp 1–6

  7. Botchway RK, Jibril AB, Kwarteng MA, Chovancova M, Oplatková ZK (2019) A review of social media posts from UniCredit bank in Europe: a sentiment analysis approach. In: Proceedings of the 3rd international conference on business and information management, pp 74–79

  8. Raghuvanshi N, Patil JM (2016) A brief review on sentiment analysis. In: 2016 international conference on electrical, electronics, and optimization techniques (ICEEOT), pp 2827–2831

  9. Hussein DM (2018) A survey on sentiment analysis challenges. J King Saud Univ Eng Sci 30:330–338

    Google Scholar 

  10. Mouthami K, Devi KN, Bhaskaran VM (2013) Sentiment analysis and classification based on textual reviews. In: 2013 international conference on Information communication and embedded systems (ICICES), pp 271–276

  11. Guzman E, Maalej W (2014) How do users like this feature? A fine grained sentiment analysis of app reviews. In: 2014 IEEE 22nd international requirements engineering conference (RE), pp 153–162

  12. Godsay M (2015) The process of sentiment analysis: a study. Int J Comput Appl 126

  13. Kumar KS, Desai J, Majumdar J (2016) Opinion mining and sentiment analysis on online customer review. In: 2016 IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–4

  14. Rahman A, Hossen MS (2019) Sentiment analysis on movie review data using machine learning approach. In: 2019 international conference on bangla speech and language processing (ICBSLP), pp 1–4

  15. Save A, Shekokar N (2020) A brief survey of sentiment analysis. In: Advanced computing technologies and applications: proceedings of 2nd international conference on advanced computing technologies and applications—ICACTA 2020, pp 353–364

  16. Clarke V, Braun V (2014) Thematic analysis. In: Teo T (ed) Encyclopedia of critical psychology. Springer, New York, pp 1947–1952

    Chapter  Google Scholar 

  17. Boyatzis RE (1998) Transforming qualitative information: thematic analysis and code development. Sage Publications Inc.

    Google Scholar 

  18. Shamantha RB, Shetty SM, Rai P (2091) Sentiment analysis using machine learning classifiers: evaluation of performance. In: 2019 IEEE 4th international conference on computer and communication systems (ICCCS), pp 21–25

  19. Bagheri A, Saraee M, de Jong F (2013) An unsupervised aspect detection model for sentiment analysis of reviews. In: Natural language processing and information systems: 18th international conference on applications of natural language to information systems, NLDB 2013, Salford, UK, 19–21 June, 2013. Proceedings 18, 2013, pp 140–151

  20. Jefferson C, Liu H, Cocea M (2017) Fuzzy approach for sentiment analysis. In: 2017 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1–6

  21. Gräbner D, Zanker M, Fliedl G, Fuchs M (2012) Classification of customer reviews based on sentiment analysis. Information and communication technologies in tourism 2012. Springer, pp 460–470

    Chapter  Google Scholar 

  22. Ranjan S, Mishra S (2020) Comparative sentiment analysis of app reviews. In: 2020 11th international conference on computing, communication and networking technologies (ICCCNT), pp 1–7

  23. Jindal K, Aron R (2021) A systematic study of sentiment analysis for social media data. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.01.048

    Article  Google Scholar 

  24. Poornima A, Priya KS (2020) A comparative sentiment analysis of sentence embedding using machine learning techniques. In: 2020 6th international conference on advanced computing and communication systems (ICACCS), pp 493–496

  25. Tyagi A, Sharma N (2018) Sentiment analysis using logistic regression and effective word score heuristic. Int J Eng Technol (UAE) 7:20–23

    Article  Google Scholar 

  26. Ramadhan W, Novianty SA, Setianingsih SC (2017) Sentiment analysis using multinomial logistic regression. In: 2017 international conference on control, electronics, renewable energy and communications (ICCREC), pp 46–49

  27. Shah A, Kothari K, Thakkar U, Khara S (2020) User review classification and star rating prediction by sentimental analysis and machine learning classifiers. In: Information and communication technology for sustainable development: proceedings of ICT4SD 2018, pp 279–288

  28. Beigi G, Hu X, Maciejewski R, Liu H (2016) An overview of sentiment analysis in social media and its applications in disaster relief. Sentiment analysis and ontology engineering: an environment of computational intelligence. Springer, pp 313–340

    Chapter  Google Scholar 

  29. Nawaz H, Ali T, Al-laith A, Ahmad I, Tharanidharan S, Nazar SKA (2018) Sentimental analysis of social media to find out customer opinion. In: Intelligent technologies and applications: first international conference, INTAP 2018, Bahawalpur, Pakistan, 23–25 Oct, 2018. Revised Selected Papers 1, 2019, pp. 110–115

  30. Charalambous M, Papagiannis P, Papasavva A, Leonidou P, Constaninou R, Terzidou L et al (2020) A privacy-preserving architecture for the protection of adolescents in online social networks. arXiv preprint arXiv:2007.12038

  31. Ghosh AK, Wisniewski P (2016) Understanding user reviews of adolescent mobile safety apps: a thematic analysis. In: Proceedings of the 19th international conference on supporting group work, pp 417–420

  32. Oyebode O, Ndulue C, Mulchandani D, Suruliraj B, Adib A, Orji FA et al (2022) COVID-19 pandemic: identifying key issues using social media and natural language processing. J Healthc Inform Res 6:174–207

    Article  Google Scholar 

  33. Alamoodi AH, Zaidan BB, Zaidan AA, Albahri OS, Mohammed KI, Malik RQ et al (2021) Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: a systematic review. Expert Syst Appl 167:114155

    Article  Google Scholar 

  34. Botchway RK, Jibril AB, Oplatková ZK, Chovancová M (2020) Deductions from a Sub-Saharan African Bank’s Tweets: a sentiment analysis approach. Cogent Econ Finance 8:1776006

    Article  Google Scholar 

  35. Bonta V, Kumaresh N, Janardhan N (2019) A comprehensive study on lexicon based approaches for sentiment analysis. Asian J Comput Sci Technol 8:1–6

    Article  Google Scholar 

  36. Tan ML, Prasanna R, Stock K, Doyle EE, Leonard G, Johnston D (2020) Modified usability framework for disaster apps: a qualitative thematic analysis of user reviews. Int J Disaster Risk Sci 11:615–629

    Article  Google Scholar 

  37. Tan ML, Prasanna R, Stock K, Hudson-Doyle E, Leonard G, Johnston D (2019) Enhancing the usability of a disaster app: exploring the perspective of the public as users. In: ISCRAM

  38. Shankar A, Behl A (2021) How to enhance consumer experience over mobile wallet: a data-driven approach. J Strateg Mark 200:1–18. https://doi.org/10.1080/0965254X.2021.1999306

    Article  Google Scholar 

  39. Kumar S, Jayant R, Charagulla N (2021) Sentiment analysis on the news to improve mental health. In: 2021 IEEE MIT undergraduate research technology conference (URTC), pp 1–5.

  40. Oyebode O, Alqahtani F, Orji R (2020) Using machine learning and thematic analysis methods to evaluate mental health apps based on user reviews. IEEE Access 8:111141–111158

    Article  Google Scholar 

  41. Alqahtani F, Orji R (2020) Insights from user reviews to improve mental health apps. Health Inform. J 26:2042–2066

    Article  Google Scholar 

  42. Olagunju T, Oyebode O, Orji R (2020) Exploring key issues affecting african mobile ecommerce applications using sentiment and thematic analysis. IEEE Access 8:114475–114486

    Article  Google Scholar 

  43. Rekanar K, O’Keeffe IR, Buckley S, Abbas M, Beecham S, Chochlov M et al (1971) Sentiment analysis of user feedback on the HSE’s Covid-19 contact tracing app. Ir J Med Sci 191:1–10

    Google Scholar 

  44. Li H, Chen Q, Zhong Z, Gong R, Han G (2022) E-word of mouth sentiment analysis for user behavior studies. Inf Process Manag 59:102784

    Article  Google Scholar 

  45. Mahmud MS, Bonny AJ, Saha U, Jahan M, Tuna ZF, Al Marouf A (2022) Sentiment analysis from user-generated reviews of ride-sharing mobile applications. In: 2022 6th international conference on computing methodologies and communication (ICCMC), pp 738–744

  46. Guntuku SC, Yaden DB, Kern ML, Ungar LH, Eichstaedt JC (2017) Detecting depression and mental illness on social media: an integrative review. Curr Opin Behav Sci 18:43–49

    Article  Google Scholar 

  47. Google Play Store Scrapper. https://github.com/digitalmethodsinitiative/google-play-scraper. Accessed 2021

  48. Elbagir S, Yang J (2019) Twitter sentiment analysis using natural language toolkit and VADER sentiment. In: Proceedings of the international multiconference of engineers and computer scientists, p 16

  49. Angelov D (2020) Top2vec: distributed representations of topics. arXiv preprint arXiv:2008.09470

  50. Miller GA (1998) WordNet: AN electronic lexical database. MIT Press

    MATH  Google Scholar 

  51. Hutto C, Gilbert E (2014) Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the international AAAI conference on web and social media, pp 216–225

  52. Dey A, Jenamani M, Thakkar JJ (2017) Lexical TF-IDF: an n-gram feature space for cross-domain classification of sentiment reviews. In: Pattern recognition and machine intelligence: 7th international conference, PReMI 2017, Kolkata, India, 5–8 Dec, 2017. Proceedings, 2017, pp 380–386

  53. Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5:1–167

    Article  Google Scholar 

  54. Dong X, Yu Z, Cao W, Shi Y, Ma Q (2020) A survey on ensemble learning. Front Comput Sci 14:241–258

    Article  Google Scholar 

  55. Sagi O, Rokach L (2018) Ensemble learning: a survey. Wiley Interdiscip Rev Data Min Knowl Discov 8:e1249

    Article  Google Scholar 

  56. Zhou J, Gandomi AH, Chen F, Holzinger A (2021) Evaluating the quality of machine learning explanations: a survey on methods and metrics. Electronics 10:593

    Article  Google Scholar 

  57. Umair M, Jabbar S, Rauf MA, Rafiq M, Mahmood T (2021) Content-based venue recommender approach for publication. In: Engineering software for modern challenges: first international conference, ESMoC 2021, Johor, Malaysia, 20–21 Oct, 2021. Revised Selected Papers, 2022, pp. 64–77

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Authors and Affiliations

  1. Department of Computer Science, National Textile University, Faisalabad, 37610, Pakistan

    Toqeer Mahmood, Saba Naseem & Rehan Ashraf

  2. Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan

    Muhammad Asif

  3. Department of Software Engineering, University of Lahore, Lahore, 54590, Pakistan

    Muhammad Umair

  4. Department of Telecommunication, Hazara University, Mansehra, 21120, Pakistan

    Mohsin Shah

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  1. Toqeer Mahmood
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  2. Saba Naseem
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  5. Muhammad Umair
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  6. Mohsin Shah
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Correspondence to Rehan Ashraf.

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Mahmood, T., Naseem, S., Ashraf, R. et al. Recognizing factors effecting the use of mobile banking apps through sentiment and thematic analysis on user reviews. Neural Comput & Applic 35, 19885–19897 (2023). https://doi.org/10.1007/s00521-023-08827-z

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