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Analysing User Experience of Mobile Banking Applications in Nigeria: A Text Mining Approach

Domaine:

socioeconomicnatural language processing

Type de record:

paper
Créateur:
Bab
Éditeur:
Cen
Hôte:
This paper analyses textual data mined from 37,460 reviews written by mobile banking application users in Nigeria over the period November 2012 – July 2020. On a scale of 1 to 5 (5 being the best), the average user rating for the twenty-two apps included in our sample is 3.5; with the apps deployed by non-interest banks having the highest average rating of 4.0 and those by commercial banks with national authorisation having the least rating of 3.4. Results from the sentiment analysis reveal that the share of positive sentiment words (17.8%) in the corpus more than double that of negative sentiment words (7.7%). Furthermore, we find that about 66 per cent of the emotions expressed by the users are associated with ‘trust’, ‘anticipation’, and ‘joy’ while the remaining 34 per cent relate to ‘surprise’, ‘fear’, ‘anger’, and ‘disgust’. These results imply that majority of the users are satisfied with their mobile banking experience. Finally, we find that the main topics contained in the user reviews pertain to (i) feedback on banks’ responsiveness to user complaints (ii) user experience regarding app functionalities and updates, and (iii) operational failures associated with the use of the apps. These results highlight the need for banks to continue to promote awareness of existing functionalities on their apps, educate users on how those solutions could be accessed, and respond to user feedback in a timely and effective manner.

Visit

doi.org

Tasks

emotion identificationsentiment analysistext classificationtopic classification

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