Sentiment Analysis on User Reviews of Snapchat in Indonesia
- 1 Department of Information Systems, School of Information Systems, Bina Nusantara University, Jakarta, Indonesia
- 2 Teknik Informatika, Institut Teknologi Dan Bisnis Indonesia, Medan, Indonesia
Abstract
This research explores the sentiment expressed in Snapchat user reviews within the Indonesian context, leveraging advanced natural language processing techniques and classification models. With a focus on the Indonesian user base, 8,015 reviews from the Google Play Store were analyzed using naive bayes, Support Vector Machines (SVM), and random forest models. The results indicated that the random forest model outperformed others with an 83% accuracy rate, followed by SVM at 81% and naive bayes at 80%. The analysis of frequently mentioned words in positive and negative reviews unveiled key aspects influencing user satisfaction. Positive reviews highlighted terms like 'bagus' (good) and 'suka' (like), while negative reviews often mentioned 'jelek' (bad) and technical issues like 'download.' The study contributes valuable insights for developers to enhance user experience on the Snapchat platform and suggests directions for future research in sentiment analysis of social media reviews.
DOI: https://doi.org/10.3844/jcssp.2025.158.167
Copyright: © 2025 Evaristus Didik Madyatmadja, Brigita Christabel Surya Winata, Evelyn Pradhan, Fathya Putri Yasmina, Feladiva Annrastia Adrian, Raihan Mahardhika, Christian and David Jumpa Malem Sembiring. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Sentiment Analysis
- User Reviews
- Naive Bayes
- Support Vector Machine
- Random Forest
- Snapchat
- Indonesia