Unpacking Public Perceptions of Qris with Twitter Data: A Vader And LDA Methodology
DOI:
https://doi.org/10.31961/eltikom.v7i2.742Keywords:
Natural Language Processing, Sentiment Analysis, Opinion Mining, Topic Modeling, Mobile Payment, QRISAbstract
QRIS, a mobile payment transaction system standardized by Bank Indonesia, has become the subject of extensive public discourse on Twitter. Employing VADER for sentiment analysis and LDA for topic modeling, this study aims to capture the nuanced perspectives of the Indonesian public toward QRIS. Our methodology includes real human validation for tweets that have been initially labeled by VADER. Our unique contributions lie in employing a mixed-methods approach for comprehensive sentiment and topic analysis, as well as making our dataset publicly available for future research. We achieve a sentiment labeling accuracy of 81.66%, uncovering that 67% of the sentiment towards QRIS is positive, 28.2% negative, and 4.17% neutral. Positive tweets mostly cover six dominant topics with a value of 0.488037, whereas negative sentiments are concentrated around three dominant topics with a  value of 0.383938. These findings not only affirm the generally positive public response towards QRIS but also highlight areas requiring attention for its continued success. Our study translates these insights into actionable recommendations, aiming to provide a multidimensional understanding that stakeholders can leverage for system enhancement. This study serves as a foundation for future works in sentiment analysis and public opinion mining related to financial technologies, particularly in the Indonesian context.
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References
A. Pal, T. Herath, and H. R. Rao, “Why do people use mobile payment technologies and why would they continue? An examination and implications from India,†Res. Policy, vol. 50, no. 6, p. 104228, 2021.
O. Balch, “Digital payments: how COVID-19 sped up adoption.†Accessed: Jan. 14, 2023. Online]. Available: https://www.raconteur.net/finance/payments/digital-payments-covid-19/
O. B. Saputri, “Preferensi konsumen dalam menggunakan quick response code indonesia standard (qris) sebagai alat pembayaran digital,†Kinerja, vol. 17, no. 2, pp. 237–247, 2020.
Tim TvOne, “QRIS Bisa Digunakan di Thailand, Transaksi jadi Lebih Mudah.†Accessed: Jan. 14, 2023. Online]. Available: https://www.tvonenews.com/ekonomi/63678-qris-bisa-digunakan-di-thailand-transaksi-jadi-lebih-mudah
Statista, “Leading countries based on number of X (formerly Twitter) users as of January 2023.†Accessed: Jan. 15, 2023. Online]. Available: https://www.statista.com/statistics/242606/number-of-active-twitter-users-in-selected-countries/
M. A. Rizaty, “Pengguna Twitter di Indonesia Capai 18,45 Juta pada 2022.†Accessed: Jan. 15, 2023. Online]. Available: https://dataindonesia.id/digital/detail/pengguna-twitter-di-indonesia-capai-1845-juta-pada-2022
L. Bing, “Sentiment analysis and opinion mining (synthesis lectures on human language technologies),†Univ. Illinois Chicago, IL, USA, 2012.
J. Albrecht, S. Ramachandran, and C. Winkler, Blueprints for Text Analytics Using Python. “ O’Reilly Media, Inc.,†2020.
V. D. Chaithra, “Hybrid approach: naive bayes and sentiment VADER for analyzing sentiment of mobile unboxing video comments,†Int. J. Electr. Comput. Eng., vol. 9, no. 5, pp. 4452–4459, 2019.
A. P. Ekaputri and S. Akbar, “Financial News Sentiment Analysis using Modified VADER for Stock Price Prediction,†in 2022 9th International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA), IEEE, 2022, pp. 1–6.
B. Mathayomchan, V. Taecharungroj, and W. Wattanacharoensil, “Evolution of COVID-19 tweets about Southeast Asian Countries: Topic modelling and sentiment analyses,†Place Brand. Public Dipl., vol. 19, no. 3, pp. 317–334, 2023.
B. S. Rintyarna et al., “Mapping acceptance of Indonesian organic food consumption under Covid-19 pandemic using Sentiment Analysis of Twitter dataset,†J. Theor. Appl. Inf. Technol, vol. 99, no. 5, pp. 1009–1019, 2021.
S. Shurrab, Y. Shannak, A. Almshnanah, H. Khazaleh, and H. Najadat, “Attitudes evaluation toward covid-19 pandemic: An application of twitter sentiment analysis and latent dirichlet allocation,†in 2021 12th International Conference on Information and Communication Systems (ICICS), IEEE, 2021, pp. 265–272.
I. Wedel, M. Palk, and S. Voß, “A bilingual comparison of sentiment and topics for a product event on Twitter,†Inf. Syst. Front., pp. 1–12, 2021.
H. Yin, X. Song, S. Yang, and J. Li, “Sentiment analysis and topic modeling for COVID-19 vaccine discussions,†World Wide Web, vol. 25, no. 3, pp. 1067–1083, 2022.
C. Hutto and E. Gilbert, “Vader: A parsimonious rule-based model for sentiment analysis of social media text,†in Proceedings of the international AAAI conference on web and social media, 2014, pp. 216–225.
T. Ali, B. Omar, and K. Soulaimane, “Analyzing tourism reviews using an LDA topic-based sentiment analysis approach,†MethodsX, vol. 9, p. 101894, 2022.
I. Vayansky and S. A. P. Kumar, “A review of topic modeling methods,†Inf. Syst., vol. 94, p. 101582, 2020.
K. M. Ridhwan and C. A. Hargreaves, “Leveraging Twitter data to understand public sentiment for the COVIDâ€19 outbreak in Singapore,†Int. J. Inf. Manag. Data Insights, vol. 1, no. 2, p. 100021, 2021.
Y. Yiran and S. Srivastava, “Aspect-based Sentiment Analysis on mobile phone reviews with LDA,†in Proceedings of the 2019 4th International Conference on Machine Learning Technologies, 2019, pp. 101–105.
JustAnotherArchivist, “snscrape.†Accessed: Oct. 08, 2023. Online]. Available: https://github.com/JustAnotherArchivist/snscrape
H. M. Keerthi Kumar and B. S. Harish, “Classification of short text using various preprocessing techniques: An empirical evaluation,†in Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, Volume 3, Springer, 2018, pp. 19–30.
W. Etaiwi and G. Naymat, “The impact of applying different preprocessing steps on review spam detection,†Procedia Comput. Sci., vol. 113, pp. 273–279, 2017.
N. A. Salsabila, Y. A. Winatmoko, A. A. Septiandri, and A. Jamal, “Colloquial indonesian lexicon,†in 2018 International Conference on Asian Language Processing (IALP), IEEE, 2018, pp. 226–229.
Y. Fauziah, B. Yuwono, and A. S. Aribowo, “Lexicon Based Sentiment Analysis in Indonesia Languages: A Systematicâ€.
S. Sbalchiero and M. Eder, “Topic modeling, long texts and the best number of topics. Some Problems and solutions,†Qual. Quant., vol. 54, pp. 1095–1108, 2020.
N. Jha and A. Mahmoud, “Mining non-functional requirements from app store reviews,†Empir. Softw. Eng., vol. 24, pp. 3659–3695, 2019.
Sastrawi, “Sastrawi.†Accessed: Jan. 17, 2023. Online]. Available: https://github.com/sastrawi/sastrawi
C. J. Hutto, “VADER-Sentiment-Analysis.†Accessed: Oct. 08, 2023. Online]. Available: https://github.com/cjhutto/vaderSentiment
F. Koto and G. Y. Rahmaningtyas, “Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs,†in 2017 International Conference on Asian Language Processing (IALP), IEEE, 2017, pp. 391–394.
S. Anastasia and I. Budi, “Twitter sentiment analysis of online transportation service providers,†in 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS), IEEE, 2016, pp. 359–365.
D. S. Putri and T. Ridwan, “Analisis Sentimen Ulasan Aplikasi Pospay dengan Algoritma Support Vector Machine,†J. Ilm. Inform., vol. 11, no. 01, pp. 32–40, 2023.
D. Mardiansyah, “Pengguna Aplikasi Dompet Digital Capai 87%.†Accessed: Mar. 06, 2023. Online]. Available: https://keuangan.kontan.co.id/news/pengguna-aplikasi-dompet-digital-capai-87
A. S. Singh and M. B. Masuku, “Sampling techniques & determination of sample size in applied statistics research: An overview,†Int. J. Econ. Commer. Manag., vol. 2, no. 11, pp. 1–22, 2014.
U. Ependi, S. Aliya, and A. Wibowo, “Sentiment Analysis of Covid-19 Handling in Indonesia Based on Lexicon Weighting,†J. Sisfokom (Sistem Inf. dan Komputer), vol. 12, no. 1, pp. 76–82, 2023.
A. P. Yulivia, “Sentiment Analysis Layanan Pesan Aantar Makanan pada Twitter Menggunakan Inset Lexicon (Studi Kasus: GoFood dan GrabFood),†2023.
R. A. Nasution, “Analisis Persepsi Pedagang Pada Penggunaan Qris Sebagai Alat Transaksi Umkm Di Kota Medan.†Universitas Islam Negeri Sumatera Utara Medan, 2020.
G. J. Tobing, L. Abubakar, and T. Handayani, “Analisis peraturan penggunaan QRIS sebagai kanal pembayaran pada praktik UMKM dalam rangka mendorong perkembangan ekonomi digital,†Acta Com. J. Huk. Kenotariatan, vol. 6, no. 03, pp. 491–509, 2021.
J. Liu, R. J. Kauffman, and D. Ma, “Competition, cooperation, and regulation: Understanding the evolution of the mobile payments technology ecosystem,†Electron. Commer. Res. Appl., vol. 14, no. 5, pp. 372–391, 2015.
Bank Indonesia, “QR Code Indonesian Standard (QRIS).†Accessed: Feb. 02, 2023. Online]. Available: https://www.bi.go.id/QRIS/default.aspx
M. A. Prayitno and W. Fadly, “Pelatihan Pemanfaatan dan Pendampingan Pembuatan QRIS (QR Code Indonesian Standard) Sebagai Media Digitalisasi ZIS di Desa Glinggang Kabupaten Ponorogo,†Bubungan Tinggi J. Pengabdi. Masy., vol. 4, no. 2, p. 543, 2022.
R. A. Hutagalung, P. Nainggolan, and P. D. Panjaitan, “Analisis Perbandingan Keberhasilan UMKM Sebelum Dan Saat Menggunakan Quick Response Indonesia Standard (QRIS) Di Kota Pematangsiantar,†J. Ekuilnomi, vol. 3, no. 2, pp. 94–103, 2021.
S. A. Natalina, A. Zunaidi, and R. Rahmah, “Quick Response Code Indonesia Standard (Qris) Sebagai Strategi Survive Usaha Mikro Kecil Dan Menengah (UMKM) Di Masa Pandemi Di Kota Kediri,†Istithmar J. Stud. Ekon. Syariah, vol. 5, no. 2, 2021.
Bank Indonesia, “Statistik Sistem Pembayaran dan Infrastruktur Pasar Keuangan (SPIP).†Accessed: Mar. 31, 2023. Online]. Available: https://www.bi.go.id/id/statistik/ekonomi-keuangan/spip/Default.aspx
I. S. Igboanusi, K. P. Dirgantoro, J.-M. Lee, and D.-S. Kim, “Blockchain side implementation of pure wallet (pw): An offline transaction architecture,†ICT Express, vol. 7, no. 3, pp. 327–334, 2021.
PT Aplikasi Karya Anak Bangsa, “Perbedaan QRIS Statis dan Dinamis yang Kelihatan Sama.†Accessed: May 31, 2023. Online]. Available: https://gobiz.co.id/pusat-pengetahuan/perbedaan-qris-statis-dan-dinamis/
W. Noviansah, “Duit dari QRIS Palsu Masuk Rekening Iman Mahlil, Bukan ‘Restorasi Masjid.’†Accessed: Apr. 22, 2023. Online]. Available: https://news.detik.com/berita/d-6668548/duit-dari-qris-palsu-masuk-rekening-iman-mahlil-bukan-restorasi-masjid
M. R. Sandi, “Permudah Transaksi, Seluruh Pasar Tradisional di Depok Sudah Terfasilitasi QRIS.†Accessed: Apr. 30, 2023. Online]. Available: https://www.idxchannel.com/economics/permudah-transaksi-seluruh-pasar-tradisional-di-depok-sudah-terfasilitasi-qris
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