Optimising SVM models in text mining to see the sentiments and user complaints of DANA mobile application through play store reviews

Arell Saverro Biyantoro, Budi Prasetiyo — Sun, 28 Sep 2025 00:00:00 +0000

Dana is a mobile electronic wallet application available for download on Google Play Store. Users can rate and comment on this application directly through the review section on the platform. By utilizing these user reviews, research can be conducted to identify the main complaints experienced by Dana application users. This research uses Support Vector Machine (SVM) sentiment analysis to classify reviews and Latent Dirichlet Allocation (LDA) to map negative comment topics. LDA extracts several representative words or tokens that are grouped to form specific themes. The findings show that the most common sources of user complaints are related to transaction issues, premium features, and app updates. These insights can provide valuable input for developers to improve the overall quality and user experience of the Dana app.

Sentiment analysis spotify applications on google play store with naïve bayes and neural network methods

Syahra Audiyani Fitra Syahra, Dwika Ananda Agustina Pertiwi — Fri, 03 Oct 2025 00:00:00 +0000

Digital advancements have significantly changed the way music is accessed and enjoyed, with streaming platforms such as Spotify emerging as one of the most widely used applications worldwide. Along with this growth, user reviews on platforms like the Google Play Store have become an important source of information, offering insights into user satisfaction and areas for improvement. In this study, sentiment analysis was conducted on Spotify reviews using two classification methods, Naïve Bayes and Neural Networks. The reviews were collected, processed, and then analyzed with both approaches to evaluate their performance. The results show that Neural Networks outperformed in terms of accuracy, F1-score, and recall, while Naïve Bayes performed better in AUC, precision, and MCC. Analysis of the dataset also revealed that negative reviews dominated at 52.8%, followed by positive at 28.3%, and neutral at 19%. These findings highlight the value of sentiment analysis in understanding user perspectives and can support developers in improving application quality and user experience.

Journal of Student Research Exploration

Optimising SVM models in text mining to see the sentiments and user complaints of DANA mobile application through play store reviews

Sentiment analysis spotify applications on google play store with naïve bayes and neural network methods