Aspect-Based Sentiment Mapping of Google Reviews for Digital Reputation Insights of Private Higher Education Institutions in Padang City
Abstract
Online reviews provide public feedback for higher education institutions, but overall star ratings cannot explain which institutional aspects shape public perception. This study aims to provide partial and aspect-specific digital reputation insights for five private higher education institutions in Padang City through aspect-based sentiment mapping of textual Google Reviews. Using Outscraper, 1,159 Google Review records were collected. After excluding 794 star-only reviews, 365 textual reviews were retained and transformed into 484 aspect-level records across five aspects: academic and lecturers, campus facilities, administrative services, environment and location, and general/others. The dataset was highly imbalanced, with 443 positive records (91.53%), 35 negative records (7.23%), and 6 neutral records (1.24%). Sentiment classification was evaluated using TF-IDF with Logistic Regression, TF-IDF with SVM, and IndoBERT through 5-fold stratified cross-validation. IndoBERT achieved the strongest performance, with an accuracy of 0.973, balanced accuracy of 0.710, macro F1-score of 0.760, and weighted F1-score of 0.971. The findings show that aspect-based sentiment mapping can support Google Review-based public perception monitoring by producing a university–aspect–sentiment map for the selected institutions. These findings are limited to textual Google Reviews from five selected institutions and should be validated using larger and more balanced datasets.
Related
- Reputation-Based Byzantine-Resilient Vector Consensus without Prespecified Fault Bounds
- Sentiment sensing: comparative analysis of multilingual sentiment dynamics
- The effects on investor sentiment and business cycles on the systematic risk of an ESG portfolio
- Fear Moves Markets: Sentiment-Augmented POMP for Volatility Modeling of Bitcoin Returns
- An Uncertainty-Aware Hybrid Symbolic-Lexicon Framework for Multi-Domain Sentiment Classification
BibTeX
@article{syaputra2026aspectbased,
title = {{Aspect-Based Sentiment Mapping of Google Reviews for Digital Reputation Insights of Private Higher Education Institutions in Padang City}},
author = {Aldo Eko Syaputra and Sopi Sapriadi and Wahyu Prima and Yofhanda Septi Eirlangga and Mutiara Oktrilisa},
journal = {Journal of Information Systems and Informatics},
year = {2026},
month = oct,
doi = {10.63158/journalisi.v8i5.1830},
url = {https://doi.org/10.63158/journalisi.v8i5.1830},
note = {CC BY},
}
RIS
TY - JOUR
TI - Aspect-Based Sentiment Mapping of Google Reviews for Digital Reputation Insights of Private Higher Education Institutions in Padang City
AU - Aldo Eko Syaputra
AU - Sopi Sapriadi
AU - Wahyu Prima
AU - Yofhanda Septi Eirlangga
AU - Mutiara Oktrilisa
PY - 2026
DA - 2026/10/24
JO - Journal of Information Systems and Informatics
DO - 10.63158/journalisi.v8i5.1830
UR - https://doi.org/10.63158/journalisi.v8i5.1830
AB - Online reviews provide public feedback for higher education institutions, but overall star ratings cannot explain which institutional aspects shape public perception. This study aims to provide partial and aspect-specific digital reputation insights for five private higher education institutions in Padang City through aspect-based sentiment mapping of textual Google Reviews. Using Outscraper, 1,159 Google Review records were collected. After excluding 794 star-only reviews, 365 textual reviews were retained and transformed into 484 aspect-level records across five aspects: academic and lecturers, campus facilities, administrative services, environment and location, and general/others. The dataset was highly imbalanced, with 443 positive records (91.53%), 35 negative records (7.23%), and 6 neutral records (1.24%). Sentiment classification was evaluated using TF-IDF with Logistic Regression, TF-IDF with SVM, and IndoBERT through 5-fold stratified cross-validation. IndoBERT achieved the strongest performance, with an accuracy of 0.973, balanced accuracy of 0.710, macro F1-score of 0.760, and weighted F1-score of 0.971. The findings show that aspect-based sentiment mapping can support Google Review-based public perception monitoring by producing a university–aspect–sentiment map for the selected institutions. These findings are limited to textual Google Reviews from five selected institutions and should be validated using larger and more balanced datasets.
SN - 2656-5935
SN - 2656-4882
ER -