ArticleData in brief2026
IGAR: Indonesian government applications review for sentiment analysis dataset.
Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- A weighted sentiment dataset for Indonesian telemedicine text classification.Data in brief · 2026Article
- IGAR: Indonesian government applications review for sentiment analysis dataset.Data in brief · 2026Article
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Authors and funding
2 authors.
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Abstract
The government has recently adopted mobile applications to enhance service delivery for citizens. However, these applications often generate mixed reactions among users. Many citizens express their opinions through reviews and ratings on the Google Play Store, providing valuable information for sentiment analysis. Leveraging this, the present paper introduces the Indonesian Government Application Review (IGAR) dataset, a collection of 617,722 user reviews from six popular government-related applications in Indonesia: Mobile JKN, MyPertamina, KAI, JMO, Satusehat, and BMKG. The reviews, originally written in Indonesian, were manually annotated as positive, neutral, or negative based on rating scores. Among the dataset, positive sentiment accounts for 336,449 reviews, negative sentiment with 246,898 reviews, while 34,375 reviews are categorized as neutral. To extend the usability of the dataset for broader research contexts, all reviews were translated into English and further processed using the Valence Aware Dictionary and sEntiment Reasoner (VADER) for automated sentiment labeling. Through VADER classification, 324,660 reviews were identified as positive, 173,329 as neutral, and 119,733 as negative. This dataset thus provides a valuable resource for advancing sentiment classification research using machine learning and deep learning model on government-related applications in Indonesia.
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Registered trials
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