Aspect-Based Sentiment Analysis with LDA and IndoBERT Algorithm on Mental Health App: Riliv
DOI:
https://doi.org/10.30871/jaic.v9i2.8958Keywords:
Aspect-Based Sentiment Analysis, IndoBERT, Latent Dirichlet Allocation , Mental Health, Sentiment AnalysisAbstract
Indonesia's mental health crisis in 2024 is increasing along with the high growth of internet users. Thus, high internet usage provides an opportunity for mobile applications including Riliv, a popular mental health application in Indonesia to become the most complete solution for overthinking, anxiety, and depression. This research aims to analyze the sentiment correlation of aspects based on App Store and Play Store reviews through scraping to effectively expose Riliv’s user satisfaction knowledge to developers using topic labeling with Latent Dirichlet Allocation (LDA) and sentiment labeling using Bidirectional Encoder Representations from Transformers (BERT) indobenchmark/indobert-base-p1 model on Aspect-Based Sentiment Analysis (ABSA). This study used 3068 reviews from September 2015 to December 2024. The main results obtained were 1) Identified the sentiment that positive is highest in 2020, neutral is highest in 2020, and negative is highest in 2018. 2) Identified 4 main aspects of the Riliv application: Access Support, Counseling Services, Meditation Features, and User Interface with LDA. 3) The majority distribution was negative on User Interface, neutral on Counseling Services, and positive on Meditation Features. 4) The effectiveness of IndoBERT compared to the non-transformer baseline algorithm. 5) The most optimal results were obtained with 70% training, 10% validation, and 20% testing, resulting in 95% accuracy.
Downloads
References
[1] Queensland Centre for Mental Health Research, “I-NAMHS: Indonesia-National Adolescent Mental Health Survey,” May 2023. Accessed: Jan. 01, 2025. [Online]. Available: https://qcmhr.org/~teoqngfi/outputs/reports/12-i-namhs-report-bahasa-indonesia
[2] N. Wusqa and S. Novitayani, “Risiko Bunuh Diri Pada Mahasiswa Tingkat Akhir,” Jurnal Ilmiah Mahasiswa Fakultas Keperawatan, vol. 6, no. 2, 2022.
[3] APJII, “Survei Penetrasi Internet Indonesia 2024,” Asosiasi Penyelenggara Jasa Internet Indonesia, 2024.
[4] R. Saphira, “Aplikasi Riliv Berbasis Synchronous Sebagai Pelayanan Dalam Cyber Counseling Di Era Digital,” Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling, vol. 3, no. 2, Dec. 2022, doi: 10.52802/crm.v3i2.483.
[5] E. H. Muktafin, Pramono, and Kusrini, “Sentiments analysis of customer satisfaction in public services using K-nearest neighbors algorithm and natural language processing approach,” TELKOMNIKA (Telecommunication, Computing, Electronics and Control), vol. 19, no. 1, pp. 146–154, Feb. 2021, doi: 10.12928/TELKOMNIKA.v19i1.17417.
[6] S. Kushwah and S. Das, “Sentiment Analysis of Big-Data in Healthcare: Issue and Challenges,” IEEE 5th International Conference on Computing Communication and Automation (ICCCA), pp. 658–663, 2020, doi: 10.1109/ICCCA49541.2020.9250841.
[7] E. Saad et al., “Determining the Efficiency of Drugs under Special Conditions from Users’ Reviews on Healthcare Web Forums,” 2021, Institute of Electrical and Electronics Engineers. doi: 10.1109/ACCESS.2021.3088838.
[8] S. A. P. Perdana, T. B. Aji, and R. Ferdiana, “Aspect Category Classification dengan Pendekatan Machine Learning Menggunakan Dataset Bahasa Indonesia (Aspect Category Classification with Machine Learning Approach Using Indonesian Language Dataset),” Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 10, no. 3, pp. 229–235, 2021, doi: 10.22146/jnteti.v10i3.1819.
[9] P. Zambrano et al., “Technical Mapping of the Grooming Anatomy Using Machine Learning Paradigms: An Information Security Approach,” nstitute of Electrical and Electronics Engineers, vol. 7, pp. 142129–142146, 2019, doi: 10.1109/ACCESS.2019.2942805.
[10] R. A. Rahman, V. H. Pranatawijaya, and N. N. K. Sari, “Analisis Sentimen Berbasis Aspek pada Ulasan Aplikasi Gojek,” KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, vol. 4, no. 1, Jun. 2024, doi: 10.24002/konstelasi.v4i1.8922.
[11] P. T. Sejati, F. Al Zami, A. Marjuni, H. Indrayani, and I. D. Puspitarini, “Aspect-Based Sentiment Analysis for Enhanced Understanding of ‘Kemenkeu’ Tweets,” Journal of Applied Informatics and Computing (JAIC), vol. 8, no. 2, pp. 487–498, 2024.
[12] D. D. Ayani, H. S. Pratiwi, and H. Muhardi, “Implementasi Web Scraping untuk Pengambilan Data pada Situs Marketplace,” Jurnal Sistem dan Teknologi Informasi (JustIN), vol. 7, no. 4, pp. 45–54, 2019, doi: 10.26418/justin.v7i4.30930.
[13] R. Rani and D. K. Lobiyal, “Performance evaluation of text-mining models with Hindi stopwords lists,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 6 Part A, pp. 2771–2786, 2022, doi: 10.1016/j.jksuci.2020.03.003.
[14] A. Luthfiarta, J. Zeniarja, and A. Salam, “Integrasi Peringkas Dokumen Otomatis Dengan Algoritma Latent Semantic Analysis (LSA) Pada Peringkas Dokumen Otomatis Untuk Proses Clustering Dokumen,” Techno.COM, vol. 13, no. 1, pp. 61–68, 2014, doi: 10.33633/tc.v13i1.543.
[15] N. Sulistiyowati and M. Jajuli, “Integrasi Naive Bayes Dengan Teknik Sampling SMOTE Untuk Menangani Data Tidak Seimbang,” vol. 14, no. 1, 2020, doi: 10.25134/nuansa.v14i1.2411.
[16] D. Septiani and I. Isabela, “Analisis Term Frequency Inverse Document Frequency (TF-IDF) Dalam Temu Kembali Informasi Pada Dokumen Teks,” vol. 1, no. 2, Mar. 2022.
[17] N. A. C.A, D. H. Citra, W. Purnama, C. Nisa, and A. R. Kurnia, “The Implementation of Naïve Bayes Algorithm for Sentiment Analysis of Shopee Reviews on Google Play Store,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 2, pp. 47–54, Apr. 2022, doi: 10.57152/malcom.v2i1.195.
[18] A. M. Mantika, A. Triayudi, and R. T. Aldisa, “Sentiment Analysis on Twitter Using Naïve Bayes and Logistic Regression for the 2024 Presidential Election,” SaNa: Journal of Blockchain, NFTs and Metaverse Technology, vol. 2, no. 1, pp. 44–55, Feb. 2024, doi: 10.58905/sana.v2i1.267.
[19] T. F. Basar, D. E. Ratnawati, and I. Arwani, “Analisis Sentimen Pengguna Twitter terhadap Pembayaran Cashless menggunakan Shopeepay dengan Algoritma Random Forest,” Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, vol. 6, no. 3, pp. 1426–1433, Mar. 2022.
[20] P. Sayarizki, Hasmawati, and H. Nurrahmi, “Implementation of IndoBERT for Sentiment Analysis of Indonesian Presidential Candidates,” Ind. Journal on Computing, vol. 9, no. 2, pp. 61–72, 2024, doi: 10.34818/indojc.2024.9.2.934.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Firda Ayu Dwi Aryanti, Ardytha Luthfiarta, Dennis Adiwinata Irwan Soeroso
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
