Aspect-Based Sentiment Analysis in Identifying Factors Causing Technostress in Fintech Users Using Naïve Bayes Algorithm

Yanuar Taruna Lutfi (), Muhardi Saputra and Riska Yanu Fa’rifah
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Yanuar Taruna Lutfi: Telkom University, Faculty of Engineering, Faculty of Industrial and System Engineering
Muhardi Saputra: Telkom University, Faculty of Engineering, Faculty of Industrial and System Engineering
Riska Yanu Fa’rifah: Telkom University, Faculty of Engineering, Faculty of Industrial and System Engineering

A chapter in Proceedings of the International Conference on Enterprise and Industrial Systems (ICOEINS 2023), 2023, pp 107-117 from Springer

Abstract: Abstract Technology has revolutionized finance through fintech, simplifying transactions and access to financial services. In Indonesia, fintech, particularly e-wallets, has experienced rapid growth. However, these technological advancements also present challenges such as technostress, which affects user behavior. Although OVO dominated the e-wallet market in 2021, user ratings declined in 2022, possibly related to news of complaints related to OVO services on the Google Play Store. This research aims to identify key aspects through data mining, using Aspect-Based Sentiment Analysis (ABSA) using Naïve Bayes with these factors, and determine the causes of technostress among OVO users. In addition, this study also evaluated text transformation methods: TF-IDF and Bag-of-Words (BoW). LDA-based topic modeling revealed 5 clusters with 4 core topics: features, access, services, and security. In general sentiment analysis, a data sharing ratio of 70:30 yielded the highest accuracy of 94.3% for TF-IDF and 95.25% for BoW compared to 75:25 and 80:20. BoW excels in terms of accuracy and prediction quality without overfitting. However, the TF-IDF model had difficulty with the prediction of positive reviews. The causes of technostress among OVO users were related to the transfer process (feature aspect), login problems (access aspect), concerns about customer service quality (service aspect), and satisfaction with security (security aspect).

Keywords: e-wallet; technostress; TF-IDF; BoW; ABSA; LDA; Naïve Bayes (search for similar items in EconPapers)
Date: 2023
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DOI: 10.2991/978-94-6463-340-5_10

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