Sentiment Analysis Classification of ChatGPT on Twitter Big Data in Indonesia Using Fast R-CNN
Abstract
Keywords
Full Text:
PDFReferences
B. D. Lund, T. Wang, N. R. Mannuru, B. Nie, S. Shimray, and Z. Wang, “ChatGPT and a new academic reality: Artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing,†J Assoc Inf Sci Technol, vol. 74, no. 5, pp. 570–581, 2023, doi: https://doi.org/10.1002/asi.24750.
J. Kleesiek, Y. Wu, G. Stiglic, J. Egger, and J. Bian, “An Opinion on ChatGPT in Health Care-Written by Humans Only,†Journal of nuclear medicine : official publication, Society of Nuclear Medicine, vol. 64, no. 5. NLM (Medline), pp. 701–703, May 01, 2023. doi: 10.2967/jnumed.123.265687.
C. K. Lo, “What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature,†Educ Sci (Basel), vol. 13, no. 4, Apr. 2023, doi: 10.3390/educsci13040410.
J. Lievens, “ARTIFICIAL INTELLIGENCE (AI) IN HIGHER EDUCATION: TOOL OR TRICKERY?,†in Education and New Developments, Jun. 2023, pp. 645–647. doi: 10.36315/2023v2end141.
Y. Akbar and T. Sugiharto, “Analisis Sentimen Pengguna Twitter di Indonesia Terhadap ChatGPT Menggunakan Algoritma C4.5 dan Naïve Bayes,†Jurnal Sains dan Teknologi, vol. 5, no. 1, pp. 115–122, 2023, doi: 10.55338/saintek.v4i3.1368.
M. Farrokhnia, S. K. Banihashem, O. Noroozi, and A. Wals, “A SWOT analysis of ChatGPT: Implications for educational practice and research,†Innovations in Education and Teaching International, 2023, doi: 10.1080/14703297.2023.2195846.
V. Taecharungroj, “‘What Can ChatGPT Do?’ Analyzing Early Reactions to the Innovative AI Chatbot on Twitter,†Big Data and Cognitive Computing, vol. 7, no. 1, Mar. 2023, doi: 10.3390/bdcc7010035.
G. Alexandridis, I. Varlamis, K. Korovesis, G. Caridakis, and P. Tsantilas, “A survey on sentiment analysis and opinion mining in greek social media,†Information (Switzerland), vol. 12, no. 8, Aug. 2021, doi: 10.3390/info12080331.
M. Bordoloi and S. K. Biswas, “Sentiment analysis: A survey on design framework, applications and future scopes,†Artif Intell Rev, pp. 1–56, Mar. 2023, doi: 10.1007/s10462-023-10442-2.
H. Kaur, S. U. Ahsaan, B. Alankar, and V. Chang, “A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets,†Information Systems Frontiers, vol. 23, no. 6, pp. 1417–1429, Dec. 2021, doi: 10.1007/s10796-021-10135-7.
S. J. Pipin and H. Kurniawan, “Analisis Sentimen Kebijakan MBKM Berdasarkan Opini Masyarakat di Twitter Menggunakan LSTM,†Jurnal SIFO Mikroskil, vol. 23, no. 2, pp. 197–208, 2022, doi: https://doi.org/10.55601/jsm.v23i2.900.
L. Yang, Y. Li, J. Wang, and R. S. Sherratt, “Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning,†IEEE, vol. 8, pp. 23522–23530, 2020, doi: 10.1109/ACCESS.2020.2969854.
U. D. Gandhi, P. Malarvizhi Kumar, G. Chandra Babu, and G. Karthick, “Sentiment Analysis on Twitter Data by Using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM),†Wirel Pers Commun, May 2021, doi: 10.1007/s11277-021-08580-3.
L. Khan, A. Amjad, K. M. Afaq, and H.-T. Chang, “Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media,†Applied Sciences, vol. 12, no. 5, p. 2694, Mar. 2022, doi: 10.3390/app12052694.
A. A. Nagra, K. Alissa, T. M. Ghazal, S. Kukunuru, M. M. Asif, and M. Fawad, “Deep Sentiments Analysis for Roman Urdu Dataset Using Faster Recurrent Convolutional Neural Network Model,†Applied Artificial Intelligence, vol. 36, no. 1, Dec. 2022, doi: 10.1080/08839514.2022.2123094.
S. J. Pipin, R. Purba, and H. Kurniawan, “Prediksi Saham Menggunakan Recurrent Neural Network (RNN-LSTM) dengan Optimasi Adaptive Moment Estimation,†Journal of Computer System and Informatics (JoSYC), vol. 4, no. 4, pp. 806–815, 2023, doi: 10.47065/josyc.v4i4.4014.
S. Khairunnisa, A. Adiwijaya, and S. Al Faraby, “Pengaruh Text Preprocessing terhadap Analisis Sentimen Komentar Masyarakat pada Media Sosial Twitter (Studi Kasus Pandemi COVID-19),†JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 5, no. 2, p. 406, Apr. 2021, doi: 10.30865/mib.v5i2.2835.
Z.-H. Zhou, “A brief introduction to weakly supervised learning,†Natl Sci Rev, vol. 5, no. 1, pp. 44–53, Jan. 2018, doi: 10.1093/nsr/nwx106.
K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,†Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/j.gltp.2022.04.020.
M. A. Rosid, A. S. Fitrani, I. R. I. Astutik, N. I. Mulloh, and H. A. Gozali, “Improving Text Preprocessing For Student Complaint Document Classification Using Sastrawi,†IOP Conf Ser Mater Sci Eng, vol. 874, no. 1, p. 012017, Jun. 2020, doi: 10.1088/1757-899X/874/1/012017.
H. N. Irmanda and Ria Astriratma, “Klasifikasi Jenis Pantun Dengan Metode Support Vector Machines (SVM),†Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 5, pp. 915–922, Oct. 2020, doi: 10.29207/resti.v4i5.2313.
M. M. Agüero-Torales, M. J. Cobo, E. Herrera-Viedma, and A. G. López-Herrera, “A cloud-based tool for sentiment analysis in reviews about restaurants on TripAdvisor,†Procedia Comput Sci, vol. 162, pp. 392–399, 2019, doi: 10.1016/j.procs.2019.12.002.
A. M. Barik, R. Mahendra, and M. Adriani, “Normalization of Indonesian-English Code-Mixed Twitter Data,†in Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), Stroudsburg, PA, USA: Association for Computational Linguistics, 2019, pp. 417–424. doi: 10.18653/v1/D19-5554.
G. M. Demirci, S. R. Keskin, and G. Dogan, “Sentiment Analysis in Turkish with Deep Learning,†in 2019 IEEE International Conference on Big Data (Big Data), IEEE, Dec. 2019, pp. 2215–2221. doi: 10.1109/BigData47090.2019.9006066.
J. Zhou, L. Liu, W. Wei, and J. Fan, “Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding,†ACM Comput Surv, vol. 55, no. 2, pp. 1–35, Feb. 2023, doi: 10.1145/3491206.
A. Patil, “Word Significance Analysis in Documents for Information Retrieval by LSA and TF-IDF using Kubeflow,†2022, pp. 335–348. doi: 10.1007/978-981-16-2126-0_29.
G. Katz et al., “The Marabou Framework for Verification and Analysis of Deep Neural Networks,†2019, pp. 443–452. doi: 10.1007/978-3-030-25540-4_26.
V. Shatravin, D. Shashev, and S. Shidlovskiy, “Applying the Reconfigurable Computing Environment Concept to the Deep Neural Network Accelerators Development,†in 2021 International Conference on Information Technology (ICIT), IEEE, Jul. 2021, pp. 842–845. doi: 10.1109/ICIT52682.2021.9491771.
Z. Abbasiantaeb and S. Momtazi, “Textâ€based question answering from information retrieval and deep neural network perspectives: A survey,†WIREs Data Mining and Knowledge Discovery, vol. 11, no. 6, Nov. 2021, doi: 10.1002/widm.1412.
Z. Zhong, L. Sun, and Q. Huo, “An anchor-free region proposal network for Faster R-CNN-based text detection approaches,†International Journal on Document Analysis and Recognition (IJDAR), vol. 22, no. 3, pp. 315–327, Sep. 2019, doi: 10.1007/s10032-019-00335-y.
Y. Xiao, X. Wang, P. Zhang, F. Meng, and F. Shao, “Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information,†Sensors, vol. 20, no. 19, p. 5490, Sep. 2020, doi: 10.3390/s20195490.
G. B. Loganathan, T. H. Fatah, E. T. Yasin, and N. I. Hamadamen, “To Develop Multi-Object Detection and Recognition Using Improved GP-FRCNN Method,†in 2022 8th International Conference on Smart Structures and Systems (ICSSS), IEEE, Apr. 2022, pp. 1–7. doi: 10.1109/ICSSS54381.2022.9782296.
DOI: https://doi.org/10.30865/mib.v7i4.6816
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 JURNAL MEDIA INFORMATIKA BUDIDARMA

This work is licensed under a Creative Commons Attribution 4.0 International License.
JURNAL MEDIA INFORMATIKA BUDIDARMA
Universitas Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com

This work is licensed under a Creative Commons Attribution 4.0 International License.