Uncovering user perceptions toward digital banks in Indonesia: A Naïve Bayes sentiment analysis of twitter data

• Main Authors: Karmagatri, Mulyani, Aziz, Clarisa Fezia Amanda, Asih, Wini Rizki Purnama, Jumbri, Isma Addi
• Format: Article
• Language: English
• Published: Little Lion Scientific Islamabad Pakistan 2023
• Online Access: http://eprints.utem.edu.my/id/eprint/27624/2/0194027112023440.PDF; http://eprints.utem.edu.my/id/eprint/27624/; https://www.jatit.org/volumes/Vol101No12/10Vol101No12.pdf
• Institution: Universiti Teknikal Malaysia Melaka

The use of digital banks in Indonesia has rapidly increased in recent years in response to the adoption of new technologies and changes in consumer behavior. User responses to digital banks vary depending on their experience throughout their transactions on the application, which may result in satisfaction or dissatisfaction. Social media platforms such as Twitter have become a space for companies to obtain textual data related to customer reviews and their brand image. In this study, data obtained from Twitter have undergone the stages of data crawling and data cleaning. The subsequent stages involved classification using the Naïve Bayes algorithm and word cloud visualization to identify the most commonly used words based on user responses. The results of this study indicate that users' positive sentiment towards digital banks is influenced by the application's ease of use, while dissatisfaction is caused by technical constraints experienced during the administrative process. The positive, negative, and neutral sentiments in this study are used to identify business opportunities for digital banks and practical implications for future digital banking services.